Automatic Individual Pig Detection and Tracking in Pig Farms.

Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task in complex scenes, due to problems of light fluctuation, similar appearances of pigs, shape deformations, and occlusions. In order to tackle these problems, we propose a robust on-line multiple pig detection and tracking method which does not require manual marking or physical identification of the pigs and works under both daylight and infrared (nighttime) light conditions. Our method couples a CNN-based detector and a correlation filter-based tracker via a novel hierarchical data association algorithm. In our method, the detector gains the best accuracy/speed trade-off by using the features derived from multiple layers at different scales in a one-stage prediction network. We define a tag-box for each pig as the tracking target, from which features with a more local scope are extracted for learning, and the multiple object tracking is conducted in a key-points tracking manner using learned correlation filters. Under challenging conditions, the tracking failures are modelled based on the relations between responses of the detector and tracker, and the data association algorithm allows the detection hypotheses to be refined; meanwhile the drifted tracks can be corrected by probing the tracking failures followed by the re-initialization of tracking. As a result, the optimal tracklets can sequentially grow with on-line refined detections, and tracking fragments are correctly integrated into respective tracks while keeping the original identifications. Experiments with a dataset captured from a commercial farm show that our method can robustly detect and track multiple pigs under challenging conditions. The promising performance of the proposed method also demonstrates the feasibility of long-term individual pig tracking in a complex environment and thus promises commercial potential.

Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs.

Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks.

Design of sports training information analysis system based on a multi-target visual model under sensor-scale spatial transformation.

In the contemporary realm of athletic training, integrating technology is a pivotal determinant for augmenting athlete performance and refining training outcomes. The amalgamation of multi-target visual modeling with sensor technology imparts an enriched stratum of sports training data. Subsequently, the sensor scale-space transformation accentuates the comprehensive apprehension of data across diverse scales and angles. Hence, within this manuscript, addressing the multi-target tracking intricacies during sports training and competition, we posit a framework that amalgamates the shortest path elucidated by the K shortest paths (KSP) methodology with the pose information emanating from the Alphapose network. This framework recognizes the athlete's shortest path through a convolutional neural network and KSP, followed by the amalgamation of these divergent data sources. The fusion unfolds by incorporating the athlete's pose information grounded in Alphapose, culminating in a comprehensive integration of the two data streams. Consequently, synthesizing alpha-derived athlete information precipitates the ultimate amalgamation of the two information streams. The accomplished fusion, premised on Alphapose, forms the bedrock for multi-target tracking, culminating in a feature-rich synthesis. Empirical results reveal that after integrating these information streams, the Multiple Object Tracking Accuracy (MOTA) index and Global Multiple Object Tracking Accuracy (GMOTA) index surpass those of the solitary information tracking methods, thereby furnishing a technical underpinning and a foundation for information fusion within prospective sports training analysis systems.

Effect of 3D-MOT training on the execution of manual dexterity skills in a population of older adults with mild cognitive impairment and mild dementia.

Computerized cognitive training tools are an alternative to preventive treatments related to cognitive impairment and aging. In this study, the transfer of 3D multiple object tracking (3D-MOT) training on manual dexterity concerning fine and gross motor skills in 38 elderly participants, half of them with mild cognitive impairment (MCI) and the other half with mild dementia (MD) was explored. A total of 36 sessions of the 3D-MOT training program were administered to the subjects. The Montreal Cognitive Assessment (MoCA) test was used to assess the baseline cognitive status of the participants. Two batteries of manual motor skills (GPT and MMDT) were applied before and after the 3D-MOT training program. The results showed an interaction effect of training and improvement in manual dexterity tests, from the first training session until the fifteenth session, and after this range of sessions, the interaction effect was lost. However, the training effect continued to the end of the thirty-six-session program. The experimental results show the effect of cognitive training on the improvement of motor skills in older adults. This type of intervention could have a broad impact on the aging population in terms of their attention, executive functions, and therefore, their quality of life.

Two-dimensional and three-dimensional multiple object tracking learning performance in adolescent female soccer players: The role of flow experience reflected by heart rate variability.

Three-dimensional multiple object tracking (3D-MOT) has been used in various fields to mimic real-life tracking, especially in perceptual-cognitive skills training for soccer. Yet, the learning efficiency in 3D-MOT tasks has not been compared with 2D-MOT. Further, whether the advantage can be reflected by heart rate variability (HRV) based on the neurovisceral integration model should also be examined. Therefore, we used both 2D- and 3D-MOT in a brief adaptive task procedure for adolescent female soccer players with HRV measurement. A faster tracking speed threshold of participants was found in the 3D- compared to 2D-MOT, as well as average tracking speed in the last training period of 3D-MOT. Moreover, lower low frequency (LF) components of HRV in the 3D-MOT indicated a flow experience, demonstrating the provision of more attentional resources. Therefore, we observed that adolescent female soccer players demonstrated higher learning efficiency in 3D-MOT tasks in virtual reality (VR) through a higher flow experience. This study examined the learning efficiency between the two MOT tasks in the soccer domain using evidence from HRV and highlighted the utility and applicability of 3D-MOT application.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.