Temporal Mapping of Surveillance Video for Indexing and Summarization

This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created

Visual Counting of Traffic Flow from a Car via Vehicle Detection and Motion Analysis

Visual traffic counting so far has been carried out by static cameras at streets or aerial pictures from sky. This work initiates a new approach to count traffic flow by using populated vehicle driving recorders. Mainly vehicles are counted by a camera moves along a route on opposite lane. Vehicle detection is first implemented in video frames by using deep learning YOLO3, and then vehicle trajectories are counted in the spatial-temporal space called motion profile. Motion continuity, direction, and detection missing are considered to avoid multiple counting of oncoming vehicles. This method has been tested on naturalistic driving videos lasting for hours. The counted vehicle numbers can be interpolated as a flow of opposite lanes from a patrol vehicle for traffic control. The mobile counting of traffic is more flexible than the traffic monitoring by cameras at street corners