PoolNet deep feature based person re-identification

Learning with Deep Neural Networks has recently reached state-of-the-art outcomes for Person Re-Identification. Effective learning can be accomplished only with efficient features robust to illumination and viewpoint changes. This paper proposes a new feature representation method called PoolNet Deep Feature (PNDF) for person re-identification with Convolution Neural Networks. The proposed CNN architecture called PoolNet consists of two Pool Added Blocks (PAB) and a Pool Concatenated Block (PCB) to extract the more sophisticated dominant and precise features for better learning towards a person's re-identification. The efficiency of the proposed method is demonstrated in terms of re-identification accuracy by implementing it on the challenging small scale & large-scale person re-identification datasets such as VIPeR, Market1501, CUHK03, GRID, and LaST.