This preprint is under consideration at EURASIP Journal on Image and Video Processing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
Pranjal Kumar
NIT Hamirpur: National Institute of Technology Hamirpur
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6167-9884
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Video understanding identifies and classifies various actions and events in the video. Many previous works, such as video annotations, have demonstrated promising success in generating general video understanding. However, a fine summary of human activities and their interactions using state-of-the-art video captioning techniques is still difficult to produce. The comprehensive explanation of human actions and collective behaviors is important information for real time CCTV video tracking, medical treatment, sports video analysis etc. This research suggests a form of video understanding that focuses primarily on identifying group activity by learning the similarities between the pair and the actors appearance. In order to measure the similarity between the pair appearances and construct an actor relations map, the Zero Mean Normalized Cross-Correlation (ZNCC) and the zeromean sum of absolute differences(ZSAD) are proposed to allow the graph convolution network (GCN) to learn how to distinguish group actions. We recommend that MNASNet be used as the backbone to retrieve features from any video frame. A visualization model is also developed to visualize every input video frame and predict individual behavior or collective activity with projected bounding boxes on a human object.
Keywords
group activity recognition, graph convolution network, video understanding, video analytics, activity recognition
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CURRENT STATUS: Under Review
Version 1
Posted 18 Mar, 2021
No community comments so far
Reviewer #3 agreed
On 31 Mar, 2021
Reviewer #2 agreed
On 28 Mar, 2021
Reviewer #1 agreed
On 28 Mar, 2021
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Received 28 Mar, 2021
Reviewers invited
Invitations sent on 15 Mar, 2021
Editor assigned
On 08 Mar, 2021
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On 08 Mar, 2021
Editor invited
On 08 Mar, 2021
First submitted
On 25 Feb, 2021
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Computer Architecture and Engineering
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This preprint is under consideration at EURASIP Journal on Image and Video Processing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Pranjal Kumar
NIT Hamirpur: National Institute of Technology Hamirpur
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6167-9884
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 18 Mar, 2021
No community comments so far
Reviewer #3 agreed
On 31 Mar, 2021
Reviewer #2 agreed
On 28 Mar, 2021
Reviewer #1 agreed
On 28 Mar, 2021
Review #? received
Received 28 Mar, 2021
Reviewers invited
Invitations sent on 15 Mar, 2021
Editor assigned
On 08 Mar, 2021
Submission checks complete
On 08 Mar, 2021
Editor invited
On 08 Mar, 2021
First submitted
On 25 Feb, 2021
Subject Areas
Computer Architecture and Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Video understanding identifies and classifies various actions and events in the video. Many previous works, such as video annotations, have demonstrated promising success in generating general video understanding. However, a fine summary of human activities and their interactions using state-of-the-art video captioning techniques is still difficult to produce. The comprehensive explanation of human actions and collective behaviors is important information for real time CCTV video tracking, medical treatment, sports video analysis etc. This research suggests a form of video understanding that focuses primarily on identifying group activity by learning the similarities between the pair and the actors appearance. In order to measure the similarity between the pair appearances and construct an actor relations map, the Zero Mean Normalized Cross-Correlation (ZNCC) and the zeromean sum of absolute differences(ZSAD) are proposed to allow the graph convolution network (GCN) to learn how to distinguish group actions. We recommend that MNASNet be used as the backbone to retrieve features from any video frame. A visualization model is also developed to visualize every input video frame and predict individual behavior or collective activity with projected bounding boxes on a human object.
Figure 1
Figure 2
Figure 3
This preprint is available for download as a PDF.
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