RibFractureSys: A gem in the face of acute rib fracture diagnoses.

Rib fracture patients, common in trauma wards, have different mortality rates and comorbidities depending on how many and which ribs are fractured. This knowledge is therefore paramount to make accurate prognoses and prioritize patient care. However, tracking 24 ribs over upwards 200+ frames in a patient's scan is time-consuming and error-prone for radiologists, especially depending on their experience. We propose an automated, modular, three-stage solution to assist radiologists. Using 9 fully annotated patient scans, we trained a multi-class U-Net to segment rib lesions and common anatomical clutter. To recognize rib fractures and mitigate false positives, we fine-tuned a ResNet-based model using 5698 false positives, 2037 acute fractures, 4786 healed fractures, and 14,904 unfractured rib lesions. Using almost 200 patient cases, we developed a highly task-customized multi-object rib lesion tracker to determine which lesions in a frame belong to which of the 12 ribs on either side; bounding box intersection over union- and centroid-based tracking, a line-crossing methodology, and various heuristics were utilized. Our system accepts an axial CT scan and processes, labels, and color-codes the scan. Over an internal validation dataset of 1000 acute rib fracture and 1000 control patients, our system, assessed by a 3-year radiologist resident, achieved 96.1% and 97.3% correct fracture classification accuracy for rib fracture and control patients, respectively. However, 18.0% and 20.8% of these patients, respectively, had incorrect rib labeling. Percentages remained consistent across sex and age demographics. Labeling issues include anatomical clutter being mislabeled as ribs and ribs going unlabeled.

FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour of Aedes aegypti mosquitoes.

Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.

Personnel Monitoring in Shipboard Surveillance Using Improved Multi-Object Detection and Tracking Algorithm.

Detecting and tracking personnel onboard is an important measure to prevent ships from being invaded by outsiders and ensure ship security. Ships are characterized by more cabins, numerous equipment, and dense personnel, so there are problems such as unpredictable personnel trajectories, frequent occlusions, and many small targets, which lead to the poor performance of existing multi-target-tracking algorithms on shipboard surveillance videos. This study conducts research in the context of onboard surveillance and proposes a multi-object detection and tracking algorithm for anti-intrusion on ships. First, this study designs the BR-YOLO network to provide high-quality object-detection results for the tracking algorithm. The shallow layers of its backbone network use the BiFormer module to capture dependencies between distant objects and reduce information loss. Second, the improved C2f module is used in the deep layer of BR-YOLO to introduce the RepGhost structure to achieve model lightweighting through reparameterization. Then, the Part OSNet network is proposed, which uses different pooling branches to focus on multi-scale features, including part-level features, thereby obtaining strong Re-ID feature representations and providing richer appearance information for personnel tracking. Finally, by integrating the appearance information for association matching, the tracking trajectory is generated in Tracking-By-Detection mode and validated on the self-constructed shipboard surveillance dataset. The experimental results show that the algorithm in this paper is effective in shipboard surveillance. Compared with the present mainstream algorithms, the MOTA, HOTP, and IDF1 are enhanced by about 10 percentage points, the MOTP is enhanced by about 7 percentage points, and IDs are also significantly reduced, which is of great practical significance for the prevention of intrusion by ship personnel.

A New Method for Non-Destructive Identification and Tracking of Multi-Object Behaviors in Beef Cattle Based on Deep Learning.

The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming.

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods.

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

MotionTrack: Learning motion predictor for multiple object tracking.

Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains a challenge. This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT. In this context, we introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor that relies solely on object trajectory information. This predictor comprehensively integrates two levels of granularity in motion features to enhance the modeling of temporal dynamics and facilitate precise future motion prediction for individual objects. Specifically, the proposed approach adopts a self-attention mechanism to capture token-level information and a Dynamic MLP layer to model channel-level features. MotionTrack is a simple, online tracking approach. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT, characterized by highly complex object motion.

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network.

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase and a simple neural network for the deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to improve tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and costs. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, a speed increase by 37.56%, and identity switches decreased by 46.81%.

An electric bicycle tracking algorithm for improved traffic management.

This paper proposes an efficient electric bicycle tracking algorithm, EBTrack, utilizing the high-precision and lightweight YOLOv7 as the target detector to enhance the efficiency of illegal detection and recognition of electric bicycles. The EBTrack effectively captures the position and trajectory of electric bicycles in complex traffic monitoring scenarios. Firstly, we introduce the feature extraction network, ResNetEB, specifically designed for feature re-identification of electric bicycles. To maintain real-time performance, feature extraction is performed only when generating new object IDs, minimizing the impact on processing speed. Secondly, for accurate target trajectory prediction, we incorporate an adaptive modulated noise scale Kalman filter. Additionally, considering the uncertainty of electric bicycle entry directions in traffic monitoring scenarios, we design a specialized matching mechanism to reduce frequent ID switching. Finally, to validate the algorithm's effectiveness, we have collected diverse video image data of electric bicycle and urban road traffic in Hefei, Anhui Province, encompassing different perspectives, time periods, and weather conditions. We have trained the proposed detector and have evaluated its tracking performance on this comprehensive dataset. Experimental results demonstrate that EBTrack achieves impressive accuracy, with 89.8 % MOTA (Multiple Object Tracking Accuracy) and 94.2 % IDF1 (ID F1-Score). Furthermore, the algorithm effectively reduces ID switching, significantly improving tracking stability and continuity.

Zebrafishtracker3D: A 3D skeleton tracking algorithm for multiple zebrafish based on particle matching.

Zebrafish are considered as model organisms in biological and medical research because of their high degree of homology with human genes. Automatic behavioral analysis of multiple zebrafish based on visual tracking is expected to improve research efficiency. However, vision-based multi-object tracking algorithms often suffer from data loss owing to mutual occlusion. In addition, simply tracking zebrafish as points is not sufficient-more detailed information, which is required for research on zebrafish behavior. In this paper, we propose Zebrafishtracker3D, which utilizes a skeleton stability strategy to reduce detection error caused by frequent overlapping of multiple zebrafish effectively and estimates zebrafish skeletons using head coordinates in the top view. Further, we transform the front- and top-view matching task into an optimization problem and propose a particle-matching method to perform 3D tracking. The robustness of the algorithm with respect to occlusion is estimated on the dataset comprising two and three zebrafish. Experimental results demonstrate that the proposed algorithm exhibits a multiple object tracking accuracy (MOTA) exceeding 90% in the top view and a 3D tracking matching accuracy exceeding 90% in the complex videos with frequent overlapping. It is noteworthy that each instance in the trace saves its skeleton. In addition, Zebrafishtracker3D is applied in the zebrafish courtship experiment, establishes the stability of the method in applications of life science, and proves that the data can be used for behavioral analysis. Zebrafishtracker3D is the first algorithm that realizes 3D skeleton tracking of multiple zebrafish simultaneously.

A Long-Term Video Tracking Method for Group-Housed Pigs.

Pig tracking provides strong support for refined management in pig farms. However, long and continuous multi-pig tracking is still extremely challenging due to occlusion, distortion, and motion blurring in real farming scenarios. This study proposes a long-term video tracking method for group-housed pigs based on improved StrongSORT, which can significantly improve the performance of pig tracking in production scenarios. In addition, this research constructs a 24 h pig tracking video dataset, providing a basis for exploring the effectiveness of long-term tracking algorithms. For object detection, a lightweight pig detection network, YOLO v7-tiny_Pig, improved based on YOLO v7-tiny, is proposed to reduce model parameters and improve detection speed. To address the target association problem, the trajectory management method of StrongSORT is optimized according to the characteristics of the pig tracking task to reduce the tracking identity (ID) switching and improve the stability of the algorithm. The experimental results show that YOLO v7-tiny_Pig ensures detection applicability while reducing parameters by 36.7% compared to YOLO v7-tiny and achieving an average video detection speed of 435 frames per second. In terms of pig tracking, Higher-Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTP), and Identification F1 (IDF1) scores reach 83.16%, 97.6%, and 91.42%, respectively. Compared with the original StrongSORT algorithm, HOTA and IDF1 are improved by 6.19% and 10.89%, respectively, and Identity Switch (IDSW) is reduced by 69%. Our algorithm can achieve the continuous tracking of pigs in real scenarios for up to 24 h. This method provides technical support for non-contact pig automatic monitoring.

Blinding and blurring the multi-object tracker with adversarial perturbations.

Adversarial attack reveals a potential imperfection in deep models that they are susceptible to being tricked by imperceptible perturbations added to images. Recent deep multi-object trackers combine the functionalities of detection and association, rendering attacks on either the detector or the association component an effective means of deception. Existing attacks focus on increasing the frequency of ID switching, which greatly damages tracking stability, but is not enough to make the tracker completely ineffective. To fully explore the potential of adversarial attacks, we propose Blind-Blur Attack (BBA), a novel attack method based on spatio-temporal motion information to fool multi-object trackers. Specifically, a simple but efficient perturbation generator is trained with the blind-blur loss, simultaneously making the target invisible to the tracker and letting the background be regarded as moving targets. We take TraDeS as our main research tracker, and verify our attack method on other excellent algorithms (i.e., CenterTrack, FairMOT, and ByteTrack) on MOT-Challenge benchmark datasets (i.e., MOT16, MOT17, and MOT20). BBA attack reduced the MOTA of TraDeS and ByteTrack from 69.1 and 80.3 to -238.1 and -357.0, respectively, indicating that it is an efficient method with a high degrees of transferability.

Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter.

Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA(2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception.

PSMOT: Online Occlusion-Aware Multi-Object Tracking Exploiting Position Sensitivity.

Models based on joint detection and re-identification (ReID), which significantly increase the efficiency of online multi-object tracking (MOT) systems, are an evolution from separate detection and ReID models in the tracking-by-detection (TBD) paradigm. It is observed that these joint models are typically one-stage, while the two-stage models become obsolete because of their slow speed and low efficiency. However, the two-stage models have naive advantages over the one-stage anchor-based and anchor-free models in handling feature misalignment and occlusion, which suggests that the two-stage models, via meticulous design, could be on par with the state-of-the-art one-stage models. Following this intuition, we propose a robust and efficient two-stage joint model based on R-FCN, whose backbone and neck are fully convolutional, and the RoI-wise process only involves simple calculations. In the first stage, an adaptive sparse anchoring scheme is utilized to produce adequate, high-quality proposals to improve efficiency. To boost both detection and ReID, two key elements-feature aggregation and feature disentanglement-are taken into account. To improve robustness against occlusion, the position-sensitivity is exploited, first to estimate occlusion and then to direct the post-process for anti-occlusion. Finally, we link the model to a hierarchical association algorithm to form a complete MOT system called PSMOT. Compared to other cutting-edge systems, PSMOT achieves competitive performance while maintaining time efficiency.

Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.

Pig husbandry constitutes a significant segment within the broader framework of livestock farming, with porcine well-being emerging as a paramount concern due to its direct implications on pig breeding and production. An easily observable proxy for assessing the health of pigs lies in their daily patterns of movement. The daily movement patterns of pigs can be used as an indicator of their health, in which more active pigs are usually healthier than those who are not active, providing farmers with knowledge of identifying pigs' health state before they become sick or their condition becomes life-threatening. However, the conventional means of estimating pig mobility largely rely on manual observations by farmers, which is impractical in the context of contemporary centralized and extensive pig farming operations. In response to these challenges, multi-object tracking and pig behavior methods are adopted to monitor pig health and welfare closely. Regrettably, these existing methods frequently fall short of providing precise and quantified measurements of movement distance, thereby yielding a rudimentary metric for assessing pig health. This paper proposes a novel approach that integrates optical flow and a multi-object tracking algorithm to more accurately gauge pig movement based on both qualitative and quantitative analyses of the shortcomings of solely relying on tracking algorithms. The optical flow records accurate movement between two consecutive frames and the multi-object tracking algorithm offers individual tracks for each pig. By combining optical flow and the tracking algorithm, our approach can accurately estimate each pig's movement. Moreover, the incorporation of optical flow affords the capacity to discern partial movements, such as instances where only the pig's head is in motion while the remainder of its body remains stationary. The experimental results show that the proposed method has superiority over the method of solely using tracking results, i.e., bounding boxes. The reason is that the movement calculated based on bounding boxes is easily affected by the size fluctuation while the optical flow data can avoid these drawbacks and even provide more fine-grained motion information. The virtues inherent in the proposed method culminate in the provision of more accurate and comprehensive information, thus enhancing the efficacy of decision-making and management processes within the realm of pig farming.

STMMOT: Advancing multi-object tracking through spatiotemporal memory networks and multi-scale attention pyramids.

Multi-object Tracking (MOT) is very important in human surveillance, sports analytics, autonomous driving, and cooperative robots. Current MOT methods do not perform well in non-uniform movements, occlusion and appearance-reappearance scenarios. We introduce a comprehensive MOT method that seamlessly merges object detection and identity linkage within an end-to-end trainable framework, designed with the capability to maintain object links over a long period of time. Our proposed model, named STMMOT, is architectured around 4 key modules: (1) Candidate proposal creation network, generates object proposals via vision-Transformer encoder-decoder architecture; (2) Scale variant pyramid, progressive pyramid structure to learn the self-scale and cross-scale similarities in multi-scale feature maps; (3) Spatio-temporal memory encoder, extracting the essential information from the memory associated with each object under tracking; and (4) Spatio-temporal memory decoder, simultaneously resolving the tasks of object detection and identity association for MOT. Our system leverages a robust spatio-temporal memory module that retains extensive historical object state observations and effectively encodes them using an attention-based aggregator. The uniqueness of STMMOT resides in representing objects as dynamic query embeddings that are updated continuously, which enables the prediction of object states with an attention mechanism and eradicates the need for post-processing. Experimental results show that STMMOT archives scores of 79.8 and 78.4 for IDF1, 79.3 and 74.1 for MOTA, 73.2 and 69.0 for HOTA, 61.2 and 61.5 for AssA, and maintained an ID switch count of 1529 and 1264 on MOT17 and MOT20, respectively. When evaluated on MOT20, it scored 78.4 in IDF1, 74.1 in MOTA, 69.0 in HOTA, and 61.5 in AssA, and kept the ID switch count to 1264. Compared with the previous best TransMOT, STMMOT achieves around a 4.58% and 4.25% increase in IDF1, and ID switching reduction to 5.79% and 21.05% on MOT17 and MOT20, respectively.

Multi-Object Tracking on SWIR Images for City Surveillance in an Edge-Computing Environment.

Although Short-Wave Infrared (SWIR) sensors have advantages in terms of robustness in bad weather and low-light conditions, the SWIR images have not been well studied for automated object detection and tracking systems. The majority of previous multi-object tracking studies have focused on pedestrian tracking in visible-spectrum images, but tracking different types of vehicles is also important in city-surveillance scenarios. In addition, the previous studies were based on high-computing-power environments such as GPU workstations or servers, but edge computing should be considered to reduce network bandwidth usage and privacy concerns in city-surveillance scenarios. In this paper, we propose a fast and effective multi-object tracking method, called Multi-Class Distance-based Tracking (MCDTrack), on SWIR images of city-surveillance scenarios in a low-power and low-computation edge-computing environment. Eight-bit integer quantized object detection models are used, and simple distance and IoU-based similarity scores are employed to realize effective multi-object tracking in an edge-computing environment. Our MCDTrack is not only superior to previous multi-object tracking methods but also shows high tracking accuracy of 77.5% MOTA and 80.2% IDF1 although the object detection and tracking are performed on the edge-computing device. Our study results indicate that a robust city-surveillance solution can be developed based on the edge-computing environment and low-frame-rate SWIR images.

A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data.

In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel's location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance.

An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks.

To protect birds, it is crucial to identify their species and determine their population across different regions. However, currently, bird monitoring methods mainly rely on manual techniques, such as point counts conducted by researchers and ornithologists in the field. This method can sometimes be inefficient, prone to errors, and have limitations, which may not always be conducive to bird conservation efforts. In this paper, we propose an efficient method for wetland bird monitoring based on object detection and multi-object tracking networks. First, we construct a manually annotated dataset for bird species detection, annotating the entire body and head of each bird separately, comprising 3737 bird images. We also built a new dataset containing 11,139 complete, individual bird images for the multi-object tracking task. Second, we perform comparative experiments using a state-of-the-art batch of object detection networks, and the results demonstrated that the YOLOv7 network, trained with a dataset labeling the entire body of the bird, was the most effective method. To enhance YOLOv7 performance, we added three GAM modules on the head side of the YOLOv7 to minimize information diffusion and amplify global interaction representations and utilized Alpha-IoU loss to achieve more accurate bounding box regression. The experimental results revealed that the improved method offers greater accuracy, with mAP@0.5 improving to 0.951 and mAP@0.5:0.95 improving to 0.815. Then, we send the detection information to DeepSORT for bird tracking and classification counting. Finally, we use the area counting method to count according to the species of birds to obtain information about flock distribution. The method described in this paper effectively addresses the monitoring challenges in bird conservation.

An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection.

This study proposes a visual tracking system that can detect and track multiple fast-moving appearance-varying targets simultaneously with 500 fps image processing. The system comprises a high-speed camera and a pan-tilt galvanometer system, which can rapidly generate large-scale high-definition images of the wide monitored area. We developed a CNN-based hybrid tracking algorithm that can robustly track multiple high-speed moving objects simultaneously. Experimental results demonstrate that our system can track up to three moving objects with velocities lower than 30 m per second simultaneously within an 8-m range. The effectiveness of our system was demonstrated through several experiments conducted on simultaneous zoom shooting of multiple moving objects (persons and bottles) in a natural outdoor scene. Moreover, our system demonstrates high robustness to target loss and crossing situations.

3D-DIoU: 3D Distance Intersection over Union for Multi-Object Tracking in Point Cloud.

Multi-object tracking (MOT) is a prominent and important study in point cloud processing and computer vision. The main objective of MOT is to predict full tracklets of several objects in point cloud. Occlusion and similar objects are two common problems that reduce the algorithm's performance throughout the tracking phase. The tracking performance of current MOT techniques, which adopt the 'tracking-by-detection' paradigm, is degrading, as evidenced by increasing numbers of identification (ID) switch and tracking drifts because it is difficult to perfectly predict the location of objects in complex scenes that are unable to track. Since the occluded object may have been visible in former frames, we manipulated the speed and location position of the object in the previous frames in order to guess where the occluded object might have been. In this paper, we employed a unique intersection over union (IoU) method in three-dimension (3D) planes, namely a distance IoU non-maximum suppression (DIoU-NMS) to accurately detect objects, and consequently we use 3D-DIoU for an object association process in order to increase tracking robustness and speed. By using a hybrid 3D DIoU-NMS and 3D-DIoU method, the tracking speed improved significantly. Experimental findings on the Waymo Open Dataset and nuScenes dataset, demonstrate that our multistage data association and tracking technique has clear benefits over previously developed algorithms in terms of tracking accuracy. In comparison with other 3D MOT tracking methods, our proposed approach demonstrates significant enhancement in tracking performances.