Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Abstract

Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.

Image recognition method for dangerous behavior of non-stop construction personnel in large airports

“…, * ∃ ∃ θφ (7) Among them, θ represents the convex feature point set, and φ represents the virtual reference component. From this, extract the contour features of dangerous behavior personnel, and then achieve dangerous behavior detection.…”

“…Reference [6] puts forward a method of violence recognition based on key frames, which treats video frames as independent events and judges violence events according to whether the number of key frames exceeds a given threshold, and uses deep learning model to remove image background and complete behavior feature recognition. Reference [7] puts forward a traffic behavior identification method in traffic video based on Kalman filter, which sets the traffic flow and turning events of different roads, tracks the vehicles in traffic video by using YOLO algorithm, and estimates and predicts the speed and position of vehicles by using Kalman filter to complete traffic flow feature identification. Reference [8] puts forward a method of cow image behavior recognition based on deep learning, which divides the data into different categories related to the most important activities in the image, and uses convolutional neural network classifier to identify cow behavior.…”

Safety of Human–Artificial Intelligence Systems: Applying Safety Science to Analyze Loopholes in Interactions between Human Organizations, Artificial Intelligence, and Individual People

“…Also, machine learning system accuracy can be improved beyond performance on fixed datasets, for example, through the incorporation of prior maps, which is an option several human organizations have established [89]. However, in practice, the functioning of artificial intelligence systems can be affected negatively by occlusion, varying lighting conditions, and multiple traffic lights at intersections [90]. Accordingly, it cannot be assumed that there will not be loopholes in the automated recognition of traffic light signals.…”

Object Detection in Traffic Videos: A Survey

“…Although appearance-based methods are better at dealing with this issue compared to motion-based methods they still struggle to detect the objects properly when occluded. There are several studies that have attempted to solve the occlusion problem in traffic surveillance applications [118], [185]- [190]. Li et al [191] tend to fuse the prior information of the Kalman filter to address the occlusion problem during the tracking stage.…”