Ahmed, Sekh Arif Dogra, Debi Prosad Kar, Samarjit Patnaik, Renuka Lee, Seung-Cheol Choi, Heeseung Nam, Gi Pyo Kim, Ig-Jae 2024-01-19T17:02:33Z 2024-01-19T17:02:33Z 2021-09-02 2020-08 1524-9050 https://pubs.kist.re.kr/handle/201004/118347 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. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC TRACKING LOOKING Query-Based Video Synopsis for Intelligent Traffic Monitoring Applications Article 10.1109/TITS.2019.2929618 1 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.21, no.8, pp.3457 - 3468 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 21 8 3457 3468 scie scopus 000554907200027 2-s2.0-85089874705 Engineering, Civil Engineering, Electrical & Electronic Transportation Science & Technology Engineering Transportation Article TRACKING LOOKING Electron tubes Surveillance Intelligent transportation systems Trajectory Streaming media Target tracking Surveillance computer vision based traffic monitoring video synopsis query video