Paper Info
Title
A Combined Multiple Action Recognition And Summarization For Surveillance Video Sequences
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.
Year
DOI
Venue
2021
10.1007/s10489-020-01823-z
APPLIED INTELLIGENCE
Keywords
DocType
Volume
Video summarization, Human action recognition, CNN, HOG, TDMap
Journal
51
Issue
ISSN
Citations 
2
0924-669X
1
PageRank 
References 
Authors
0.39
0
5
Name
Order
Citations
PageRank
Omar ElHarrouss1226.17
Noor Almaadeed2366.71
Somaya Al-máadeed341147.43
Ahmed Bouridane483799.53
Azeddine Beghdadi556283.96