Person Re-identification Using Visual Attention
This addresses the challenge of varying appearance in person re-identification for surveillance and security applications, representing a novel method for a known bottleneck.
The paper tackled the person re-identification problem by proposing a gradient-based attention mechanism in a deep convolutional neural network, which selectively focuses on sensitive image parts with high resolution. The method outperformed state-of-the-art approaches on CUHK01, CUHK03, and Market 1501 datasets.
Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person's appearance can vary significantly when large variations in view angle, human pose, and illumination are involved. In this paper, we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem. Our model learns to focus selectively on parts of the input image for which the networks' output is most sensitive to and processes them with high resolution while perceiving the surrounding image in low resolution. Extensive comparative evaluations demonstrate that the proposed method outperforms state-of-the-art approaches on the challenging CUHK01, CUHK03, and Market 1501 datasets.