A combined multiple action recognition and summarization for surveillance video sequences
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A combined multiple action recognition and summarization for surveillance video sequences Omar Elharrouss1 · Noor Almaadeed1 · Somaya Al-Maadeed1 · Ahmed Bouridane2 · Azeddine Beghdadi3
© The Author(s) 2020
Abstract 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. Keywords Video summarization · Human action recognition · CNN · HOG · TDMap
1 Introduction Currently, video technologies are facing several challenges and difficulties, mainly attributed to the extraction of information in real-time from a large number. The extracted information can be useful to identify and detect many events that can help in many analyses, such as abnormal events and people’s behavior, as well as to predict events that usually happen in the scenes. Recently, a number of researchers focused their studies on finding effective techniques to summarize useful information from videos. This research field is essential for the improvement of video surveillance systems that require large storage space and complex data analysis, considering that data is captured 24 hours a day and 7 days a week. Therefore, summarization of video data is required in such systems to simplify data
Omar Elharrouss
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Extended author information available on the last page of the article.
analysis, facilitate information storage, and to improve the access to each time video. The summarization process can also be related to the type of scene (private or public) where the data analysis depends on whether the scene is dynamic or static, as well as whether it is crowded or uncrowded. Since the summarization process should consume less
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