Abstract
For most top view surveillance scenes, due to having little pose variations and being robust to partial occlusion, people’s head-shoulder Omega-like shapes are proven to be good cues for human detection. In this paper, we focus on learning Omega-shape features with improved discriminative ability in human detection. Orthogonal non-negative matrix factorization (ONMF) is introduced to model the local semantic parts of Omega-like shapes, which is much more robust to noise corruption and local occlusion. The properties only allowing additive, not subtractive combinations of ONMF can well suppress some background clutter. Furthermore, we introduce metric learning into SVM decision framework where object/nonobject classification is performed within a learned feasible metric space. Experimental results on a number of challenging datasets demonstrate the effectiveness and robustness of the proposed human detection method.
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This work was supported by the National Natural Science Foundation of China under Grant NSFC 61472063.
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Liu, P., Zhou, X., Cai, S. (2016). Omega-Shape Feature Learning for Robust Human Detection. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_25
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DOI: https://doi.org/10.1007/978-981-10-3002-4_25
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