Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment.

This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances. This research contributes to the field of intelligent transportation by offering a practical and efficient approach to 3D bounding box estimation using monovision sensors. We validated feasibility and applicability are through real-world road images captured by CCTV cameras.

Vision-Based Pedestrian's Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System.

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users' risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

Analyzing vehicle-pedestrian interactions: Combining data cube structure and predictive collision risk estimation model.

Road traffic accidents are a severe threat to human lives, particularly to vulnerable road users (VRUs) such as pedestrians causing premature deaths. Therefore, it is necessary to devise systems to prevent accidents in advance and respond proactively, using potential risky situations as one of the surrogate safety measurements. This study introduces a new concept of a pedestrian safety system that combines the field and the centralized processes. The system can warn of upcoming risks immediately in the field and improve the safety of risk-frequent areas by assessing the safety levels of roads without actual collisions. In particular, this study focuses on the latter by introducing a new analytical framework for a crosswalk safety assessment with various behaviors of vehicles/pedestrians and environmental features. We obtain these behavioral features from actual traffic video footages in the city with complete automatic processing. The proposed framework mainly analyzes these behaviors in multi-dimensional perspectives by constructing a data cube structure, which combines the Long Short-Term Memory (LSTM)-based predictive collision risk (PCR) estimation model and the on-line analytical processing (OLAP) operations. From the PCR estimation model, we categorize the severity of risks as four levels; "relatively safe," "caution," "warning," and "danger," and apply the proposed framework to assess the crosswalk safety with behavioral features. With the proposed framework, the various descriptive results are harvested, but we aim at conducting analysis based on two scenarios in our analytic experiments; the movement patterns of vehicles and pedestrians by road environment and the relationships between risk levels and car speeds. Consequently, the proposed framework can support decision-makers (e.g., urban planners, safety administrators) by providing the valuable information to improve pedestrian safety for future accidents, and it can help us better understand cars' and pedestrians' proactive behavior near the crosswalks. In order to confirm the feasibility and applicability of the proposed framework, we implement and apply it to actual operating CCTVs in Osan City, Republic of Korea.

SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP.

In the past decade, the number of road traffic accidents and fatalities has remained about the same level. One of strategies to protect vulnerable road users (VRUs) is to analyze the factors that cause traffic accident and then to deploy safety facilities. However, most traffic safety systems currently in operation rely on historical data, which is post-facto approach. Thus, it is necessary to prevent accident in advance and to respond in proactive manner before the accident. In this study, we propose a framework for potential pedestrian risk analysis using a multi-dimensional on-line analytical processing (OLAP), called SafetyCube, which enables decision-makers to understand the situations by scrutinizing interactive behaviors between vehicle and pedestrian. First, we collect the behavioral features of traffic-related objects (e.g., vehicles and pedestrians) extracted from closed circuit televisions (CCTVs) deployed on crosswalks throughout the overall urban, and accumulate them in a data warehouse over an extended period in order to construct a data cube model. Then, we conduct comprehensive analyses in multi-dimensional perspective using OLAP operations by varying the abstraction levels. Our analytical experiments are based on three scenarios, and the results show that the vehicle's movement patterns before entering the crosswalk, patterns of changes in speed of vehicles approaching to pedestrians, and so on. Through these results from the proposed analytical system, decision-makers can gain a better understanding of how the vehicles and pedestrians behave near the crosswalk by visualizing their interactions. Further, these insights would be reflected to improve the road environment safer. In order to validate the feasibility and applicability of the proposed system, we apply it to various crosswalks in Osan city, South Korea.

Stress detection and classification of laying hens by sound analysis.

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.