In:
ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 16, No. 4 ( 2020-11-30), p. 1-21
Abstract:
Video-based person re-identification (ReID) aims at re-identifying a specified person sequence from videos that were captured by disjoint cameras. Most existing works on this task ignore the quality discrepancy across frames by using all video frames to develop a ReID method. Additionally, they adopt only the person self-characteristic as the representation, which cannot adapt to cross-camera variation effectively. To that end, we propose a novel correlation discrepancy insight network for video-based person ReID, which consists of an unsupervised correlation insight model (CIM) for video purification and a discrepancy description network (DDN) for person representation. Concretely, CIM is constructed by using kernelized correlation filters to encode person half-parts, which evaluates the frame quality by the cross correlation across frames for selecting discriminative video fragments. Furthermore, DDN exploits the selected video fragments to generate a discrepancy descriptor using a compression network, which aims at employing the discrepancies with other persons’ to facilitate the representation of the target person rather than only using the self-characteristic. Due to the advantage in handling cross-domain variation, the discrepancy descriptor is expected to provide a new pattern for the object representation in cross-camera tasks. Experimental results on three public benchmarks demonstrate that the proposed method outperforms several state-of-the-art methods.
Type of Medium:
Online Resource
ISSN:
1551-6857
,
1551-6865
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2020
detail.hit.zdb_id:
2182650-X
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