A combined multiple action recognition and summarization for surveillance video sequences
<p>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...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513567939624960 |
|---|---|
| author | Omar Elharrouss (14150895) |
| author2 | Noor Almaadeed (14150898) Somaya Al-Maadeed (5178131) Ahmed Bouridane (2270131) Azeddine Beghdadi (14150901) |
| author2_role | author author author author |
| author_facet | Omar Elharrouss (14150895) Noor Almaadeed (14150898) Somaya Al-Maadeed (5178131) Ahmed Bouridane (2270131) Azeddine Beghdadi (14150901) |
| author_role | author |
| dc.creator.none.fl_str_mv | Omar Elharrouss (14150895) Noor Almaadeed (14150898) Somaya Al-Maadeed (5178131) Ahmed Bouridane (2270131) Azeddine Beghdadi (14150901) |
| dc.date.none.fl_str_mv | 2022-11-22T21:12:53Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10489-020-01823-z |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_combined_multiple_action_recognition_and_summarization_for_surveillance_video_sequences/21597177 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Video summarization Human action recognition CNN HOG TDMap |
| dc.title.none.fl_str_mv | A combined multiple action recognition and summarization for surveillance video sequences |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Applied Intelligence<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10489-020-01823-z" target="_blank">http://dx.doi.org/10.1007/s10489-020-01823-z</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8c0789d5842df980027a5e32f356b99e |
| identifier_str_mv | 10.1007/s10489-020-01823-z |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597177 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A combined multiple action recognition and summarization for surveillance video sequencesOmar Elharrouss (14150895)Noor Almaadeed (14150898)Somaya Al-Maadeed (5178131)Ahmed Bouridane (2270131)Azeddine Beghdadi (14150901)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationVideo summarizationHuman action recognitionCNNHOGTDMap<p>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.</p><h2>Other Information</h2> <p> Published in: Applied Intelligence<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10489-020-01823-z" target="_blank">http://dx.doi.org/10.1007/s10489-020-01823-z</a></p>2022-11-22T21:12:53ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10489-020-01823-zhttps://figshare.com/articles/journal_contribution/A_combined_multiple_action_recognition_and_summarization_for_surveillance_video_sequences/21597177CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215971772022-11-22T21:12:53Z |
| spellingShingle | A combined multiple action recognition and summarization for surveillance video sequences Omar Elharrouss (14150895) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Video summarization Human action recognition CNN HOG TDMap |
| status_str | publishedVersion |
| title | A combined multiple action recognition and summarization for surveillance video sequences |
| title_full | A combined multiple action recognition and summarization for surveillance video sequences |
| title_fullStr | A combined multiple action recognition and summarization for surveillance video sequences |
| title_full_unstemmed | A combined multiple action recognition and summarization for surveillance video sequences |
| title_short | A combined multiple action recognition and summarization for surveillance video sequences |
| title_sort | A combined multiple action recognition and summarization for surveillance video sequences |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Video summarization Human action recognition CNN HOG TDMap |