A combined multiple action recognition and summarization for surveillance video sequences

Elharrouss, Omar, Almaadeed, Noor, Al-Maadeed, Somaya, Bouridane, Ahmed and Beghdadi, Azeddine (2021) A combined multiple action recognition and summarization for surveillance video sequences. Applied Intelligence, 51 (2). pp. 690-712. ISSN 0924-669X

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Official URL: https://doi.org/10.1007/s10489-020-01823-z


Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies’ silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

Item Type: Article
Additional Information: Funding Information: Open Access funding provided by the Qatar National Library. This publication was made by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Uncontrolled Keywords: Video summarization, Human action recognition, CNN, HOG, TDMap
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 21 Jan 2022 10:19
Last Modified: 21 Jan 2022 10:30
URI: http://nrl.northumbria.ac.uk/id/eprint/48221

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