Image-to-video person re-identification using three-dimensional semantic appearance alignment and cross-modal interactive learning

Abstract

Image-to-video person re-identification (I2V ReID), which aims to retrieve human targets between image-based queries and video-based galleries, has recently become a new research focus. However, the appearance misalignment and modality misalignment in both images and videos caused by pose variations, camera views, misdetections, and different data types, make I2V ReID still challenging. To this end, we propose a deep I2V ReID pipeline based on three-dimensional semantic appearance alignment (3D-SAA) and cross-modal interactive learning (CMIL) to address the aforementioned two challenges. Specifically, in the 3D-SAA module, the aligned local appearance images extracted by dense 3D human appearance estimation are in conjunction with global image and video embedding streams to learn more fine-grained identity features. The aligned local appearance images are further semantically aggregated by the proposed multi-branch aggregation network to weaken the negligible body parts. Moreover, to overcome the influence of modality misalignment, a CMIL module enables the communication between global image and video streams by interactively propagating the temporal information in videos to the channels of image feature maps. Extensive experiments on challenging MARS, DukeMTMC-VideoReID and iLIDS-VID datasets, show the superiority of our approach.