Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences
With the increase in demand for video conferencing and IOT applications, efficient video coding standards are necessary. The performance of MPEG-4 coding scheme depends on the efficiency of the video object plane (VOP) generation methods. In head and shoulder video, such as news reading, video confe...
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| Format: | Article |
| Language: | English |
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Springer
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820306273 |
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| author | Prabodh Kumar Sahoo Priyadarshi Kanungo Satyasis Mishra Bibhu Prasad Mohanty |
| author_facet | Prabodh Kumar Sahoo Priyadarshi Kanungo Satyasis Mishra Bibhu Prasad Mohanty |
| author_sort | Prabodh Kumar Sahoo |
| collection | DOAJ |
| description | With the increase in demand for video conferencing and IOT applications, efficient video coding standards are necessary. The performance of MPEG-4 coding scheme depends on the efficiency of the video object plane (VOP) generation methods. In head and shoulder video, such as news reading, video conferencing video sequences, the object has a very little movement in between two consecutive frames. Therefore, traditional segmentation methods could not extract the complete VOP efficiently. In this paper, we propose an efficient spatiotemporal segmentation method to extract the moving object for the generation of VOP in head and shoulder video sequences. First, a motion map of the object at each frame is generated based on the entropy of the temporal change of each pixel. Secondly, each frame is spatially segmented based on peak-means clustering approach. Finally, both motion map and spatial segmentation information are fused to extract the complete shape of the slow moving object. Experimental outcome depicts that the proposed method has highest detection accuracy with average intersection of union (IOU) score of 94.32% per frame and F1 measure of 97.75% per frame. |
| format | Article |
| id | doaj-art-d04a9e10b7b64000971fc9b4c3a86d96 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-d04a9e10b7b64000971fc9b4c3a86d962025-08-20T03:52:02ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-09-013485296530410.1016/j.jksuci.2020.12.019Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequencesPrabodh Kumar Sahoo0Priyadarshi Kanungo1Satyasis Mishra2Bibhu Prasad Mohanty3Department of Electrical and Electronics Engineering, C.V. Raman Global University, Bhubaneswar, Odisha 752054, India; Corresponding author.Department of Electronics and Telecommunication Engineering, C.V. Raman Global University, Bhubaneswar, Odisha 752054, IndiaDepartment of Electronics and Communication Engineering, Adama Science and Technology University, EthiopiaDepartment of Electronics and Communication Engineering, ITER, SOA University, Khandagiri, Bhubaneswar, Odisha 751030, IndiaWith the increase in demand for video conferencing and IOT applications, efficient video coding standards are necessary. The performance of MPEG-4 coding scheme depends on the efficiency of the video object plane (VOP) generation methods. In head and shoulder video, such as news reading, video conferencing video sequences, the object has a very little movement in between two consecutive frames. Therefore, traditional segmentation methods could not extract the complete VOP efficiently. In this paper, we propose an efficient spatiotemporal segmentation method to extract the moving object for the generation of VOP in head and shoulder video sequences. First, a motion map of the object at each frame is generated based on the entropy of the temporal change of each pixel. Secondly, each frame is spatially segmented based on peak-means clustering approach. Finally, both motion map and spatial segmentation information are fused to extract the complete shape of the slow moving object. Experimental outcome depicts that the proposed method has highest detection accuracy with average intersection of union (IOU) score of 94.32% per frame and F1 measure of 97.75% per frame.http://www.sciencedirect.com/science/article/pii/S1319157820306273Temporal differencingSegmentationEntropyClusteringMoving object detection |
| spellingShingle | Prabodh Kumar Sahoo Priyadarshi Kanungo Satyasis Mishra Bibhu Prasad Mohanty Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences Journal of King Saud University: Computer and Information Sciences Temporal differencing Segmentation Entropy Clustering Moving object detection |
| title | Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences |
| title_full | Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences |
| title_fullStr | Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences |
| title_full_unstemmed | Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences |
| title_short | Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences |
| title_sort | entropy feature and peak means clustering based slowly moving object detection in head and shoulder video sequences |
| topic | Temporal differencing Segmentation Entropy Clustering Moving object detection |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820306273 |
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