AE-Net: Appearance-Enriched Neural Network With Foreground Enhancement for Person Re-Identification

Shangdong Zhu, Yunzhou Zhang, Yixiu Liu, Yu Feng, Sonya A. Coleman, Dermot Kerr

Abstract: Person re-identification (Re-ID) in environments subject to intensive appearance and background variations due to seasons, weather conditions, illumination and human factors is a challenging task. A wide variety of existing algorithms address this problem either for appearance changes or background clutter, but neglect to explore a powerful framework to consider solving both cases simultaneously. To overcome this limitation, this research introduces an effective appearance-enriched neural network (AE-Net) with foreground enhancement based on generative adversarial nets (GANs) and an attention mechanism to enrich the appearance of person images while suppressing the influence of the background. Specifically, a channel-grouped convolution and squeeze weighted (CGCSW) module is first proposed to extract the powerful feature representation of individuals. Secondly, a foreground-enhanced and background-suppressed (FEBS) module is proposed to enhance the foreground of individual samples while weakening the impact of the background. Thirdly, A stage-wise consistency loss is presented to enable our model maintain consistent foreground-enhanced and background-suppressed stages. Finally, this study evaluates the proposed method and compares it with state-of-the-art approaches on three public datasets. The experimental results demonstrate the effectiveness and improvements achieved by using the presented architecture.