A combined multiple action recognition and summarization for surveillance video sequences

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...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021-02, Vol.51 (2), p.690-712
Main Authors: Elharrouss, Omar, Almaadeed, Noor, Al-Maadeed, Somaya, Bouridane, Ahmed, Beghdadi, Azeddine
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Computer vision
Crime
Frames (data processing)
Histograms
Human activity recognition
Human motion
Machines
Manufacturing
Mechanical Engineering
Motion perception
Processes
Surveillance
Training
Video data
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Summary: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.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01823-z