Query-Based Video Synopsis for Intelligent Traffic Monitoring Applications

Abstract

Synopsis of a long-duration video has many applications in intelligent transportation systems. It can help to monitor traffic with lesser manpower. However, generating meaningful synopsis of a long-duration video recording can be challenging. Often summarized outputs include redundant contents or activities that may not be helpful to the observer. Moving object trajectories are possible sources of information that can be used to generate the synopsis of long-duration videos. The synopsis generation faces challenges due to object tracking, grouping of the trajectories with respect to activity type, object category, and contextual information, and generating smooth synopsis according to a query. In this paper, we propose a method to generate meaningful and smooth synopsis of long-duration videos according to the users' query. We have tracked moving objects and adopted deep learning to classify the objects into known categories (e.g., car, bike, and pedestrians). We then identify regions in the surveillance scene with the help of unsupervised clustering. Each tube (spatiotemporal object trajectory) is represented by the source and the destination. In the final stage, we take a query from the user and generate the synopsis video by smoothly blending the appropriate tubes over the background frame through energy minimization. The proposed method has been evaluated on two publicly available datasets and our own surveillance datasets. We have compared the method with popular state-of-the-art techniques. The experiments reveal that the proposed method is superior to the existing techniques and it produces visually seamless video synopsis.