Sparse Representation for Crowd Attributes Recognition
| Autor: | Mohd Naufal Saad, Nidal Kamel, Ibrahima Faye, Yasir Salih Ali, Aliyu Nuhu Shuaibu, Aamir Saeed Malik |
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| Rok vydání: | 2017 |
| Předmět: |
General Computer Science
Computer science Human behavior Feature extraction 02 engineering and technology Machine learning computer.software_genre histogram of oriented gradient Histogram 0202 electrical engineering electronic engineering information engineering artificial intelligence and sparse coding General Materials Science crowd scenes K-SVD business.industry General Engineering 020207 software engineering Pattern recognition Sparse approximation Visualization Support vector machine histogram of optical flow 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business Neural coding lcsh:TK1-9971 computer |
| Zdroj: | IEEE Access, Vol 5, Pp 10422-10433 (2017) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2017.2708838 |
| Popis: | Human behavior analysis has become a critical area of research in computer vision and artificial intelligence research community. In recent years, video surveillance systems of crowd scenes have witnessed an increased demand in different applications, such as safety, security, entertainment, and personal mental health. Although many methods have been proposed, certain limitations exist, and many unresolved issues remain open. In this paper, we proposed a novel spatio-temporal sparse coding representation, based on sparse coded features with $k$ -means singular value decomposition for robust classification of crowd behaviors. Extensive experiments have shown that dictionary learning method with sparsely coded features captured vital structures of video scenes and yielded discriminant descriptors for classifications than conventional bag-of-visual-features. Relying on the measurable features of crowd scenes and motion characteristics, we can represent different attributes of the crowd scenes. Experiments on hundreds of video scenes were carried out on publicly available datasets. Quantitative evaluation indicates that the proposed model display superior accuracy, precision, and recall in classifying human behaviors with linear support vector machine when compared with the state-of-the-art methods. The proposed method is conceptually simple and easy to train: thereby achieving an accuracy of 93.50%, a precision of 93.40%, and a recall of 95.96%. |
| Databáze: | OpenAIRE |
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