Abstract
Multiview object classification and recognition is of great importance in many applications such as computer vision and robotics by Kuo and Nevatia (Applications of computer vision (WACV), p. 18, 2009). This paper focuses on the specific case of multiview pedestrian image classification. The contributions of this paper are twofold. First, we collected a new multiview pedestrian dataset, which has been manually labeled with viewpoint, posture, and scene category tags. Second, a nonnegative least square (NNLS)-based multiview pedestrian image classification method is presented by Pang et al. (IEEE transactions on image processing, vol 20, pp. 1388–1400). Experimental results demonstrate that the proposed method is robust and effective.
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Acknowledgments
The authors would like to thank Mr. Guanliang Zhang for collecting and organizing the multiview pedestrian dataset. This work was supported in part by the National Natural Science Foundation of China under Grant 61303186, 61240058, and by the Ph.D. Programs Foundation of Ministry of Education of China under Grant 20124307120013.
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Wu, L., Sun, H., Ji, K., Fan, Y., Zhang, Y. (2015). Multiview Image Classification via Nonnegative Least Squares. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_21
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DOI: https://doi.org/10.1007/978-3-662-46469-4_21
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