Human detection framework for automated surveillance systems
Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existin...
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Institute of Advanced Engineering and Science
2016
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| author | Noaman, Redwan A. K. Mohd. Ali, Mohd. Alauddin Zainal, Nasharuddin Saeed, Faisal |
| author_facet | Noaman, Redwan A. K. Mohd. Ali, Mohd. Alauddin Zainal, Nasharuddin Saeed, Faisal |
| author_sort | Noaman, Redwan A. K. |
| collection | ePrints |
| description | Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas-Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system. |
| first_indexed | 2024-03-05T20:04:27Z |
| format | Article |
| id | utm.eprints-72671 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:04:27Z |
| publishDate | 2016 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | utm.eprints-726712017-11-23T01:37:09Z http://eprints.utm.my/72671/ Human detection framework for automated surveillance systems Noaman, Redwan A. K. Mohd. Ali, Mohd. Alauddin Zainal, Nasharuddin Saeed, Faisal QA75 Electronic computers. Computer science Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas-Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system. Institute of Advanced Engineering and Science 2016 Article PeerReviewed Noaman, Redwan A. K. and Mohd. Ali, Mohd. Alauddin and Zainal, Nasharuddin and Saeed, Faisal (2016) Human detection framework for automated surveillance systems. International Journal of Electrical and Computer Engineering, 6 (2). pp. 877-886. ISSN 2088-8708 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960157534&doi=10.11591%2fijece.v6i1.9578&partnerID=40&md5=42dcc44d20478684507f50446ff43ff0 |
| spellingShingle | QA75 Electronic computers. Computer science Noaman, Redwan A. K. Mohd. Ali, Mohd. Alauddin Zainal, Nasharuddin Saeed, Faisal Human detection framework for automated surveillance systems |
| title | Human detection framework for automated surveillance systems |
| title_full | Human detection framework for automated surveillance systems |
| title_fullStr | Human detection framework for automated surveillance systems |
| title_full_unstemmed | Human detection framework for automated surveillance systems |
| title_short | Human detection framework for automated surveillance systems |
| title_sort | human detection framework for automated surveillance systems |
| topic | QA75 Electronic computers. Computer science |
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