The Research of Group Re-identification from Multiple Cameras

• URL: http://arxiv.org/abs/2407.14620v1
• Date: Fri, 19 Jul 2024 18:28:13 GMT
• Title: The Research of Group Re-identification from Multiple Cameras
• Authors: Hao Xiao,
• Abstract summary: Group re-identification is very challenging since it is not only interfered by view-point and human pose variations in the traditional re-identification tasks. This paper introduces a novel approach which leverages the multi-granularity information inside groups to facilitate group re-identification.
• Score: 0.4955551943523977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object re-identification is of increasing importance in visual surveillance. Most existing works focus on re-identify individual from multiple cameras while the application of group re-identification (Re-ID) is rarely discussed. We redefine Group Re-identification as a process which includes pedestrian detection, feature extraction, graph model construction, and graph matching. Group re-identification is very challenging since it is not only interfered by view-point and human pose variations in the traditional re-identification tasks, but also suffered from the challenges in group layout change and group member variation. To address the above challenges, this paper introduces a novel approach which leverages the multi-granularity information inside groups to facilitate group re-identification. We first introduce a multi-granularity Re-ID process, which derives features for multi-granularity objects (people/people-subgroups) in a group and iteratively evaluates their importances during group Re-ID, so as to handle group-wise misalignments due to viewpoint change and group dynamics. We further introduce a multi-order matching scheme. It adaptively selects representative people/people-subgroups in each group and integrates the multi-granularity information from these people/people-subgroups to obtain group-wise matching, hence achieving a more reliable matching score between groups. Experimental results on various datasets demonstrate the effectiveness of our approach.

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