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Human instance guided two-stream network for person reidentification

Abstract. Since the person reidentification (Re-ID) technique was proposed, it has successively produced successful research. The performance of this technique still has room for improvement due to the challenges of cluttered background and spatial misalignment. Recently, researchers have attempted to integrate human parsing or pose estimation results to capture person regions for mitigating these problems. However, many important and recognizable cues remain in the background regions. To mine these useful cues while still noticing the informative human body parts, this work proposes a simple yet effective two-stream model that utilizes one stream from human instance to learn discriminative human part features and enriches the representation with the other stream from the original image. Moreover, to alleviate the spatial misalignment problem, this paper rectifies the feature extraction regions with the assistance of human segmentation results. Experimental results demonstrate that the proposed method significantly improves the performance of person Re-ID.