Abstract
Person Re-Identification is the problem of matching people across a network of non-overlapping cameras. One of the challenges is how to match body parts to body parts for comparison between images of two people in the context of different viewpoints as well as deformable human bodies. Existing approaches usually use fixed models to localize body parts or detect human shapes to extract body parts from the shapes. Therefore, it is difficult to change to a new model or structure of body parts. Moreover, those approaches could not deal with multiple human poses simultaneously. We propose a machine learning-based method to extract body parts that is based on Deformable Part Models (DPM). DPM is easy to train and has robust performance. In addition, with DPM, we could use multiple models for multiple human poses concurrently. Experiments on standard dataset ETHZ1 show that the proposed method outperforms state of the art methods.
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Nguyen, VH., Nguyen, K., Le, DD., Duong, D.A., Satoh, S. (2013). Person Re-identification Using Deformable Part Models. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_76
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DOI: https://doi.org/10.1007/978-3-642-42051-1_76
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