Sparse coding with cross-view invariant dictionaries for person re-identification

作者：Xu, Yunlu; Guo, Jie^*; Huang, Zheng; Qiu, Weidong

来源：Multimedia Tools and Applications, 2018, 77(9): 10715-10732.

DOI：10.1007/s11042-017-4893-5

• Dictionary learning
• Sparse coding
• Person re-identification
• Intelligent surveillance

The task of matching observations of the same person in disjoint views captured by non-overlapping cameras is known as the person re-identification problem. It is challenging owing to low-quality images, inter-object occlusions, and variations in illumination, viewpoints and poses. Unlike previous approaches that learn Mahalanobis-like distance metrics, we propose a novel approach based on dictionary learning that takes the advances of sparse coding of discriminatingly and cross-view invariantly encoding features representing different people. Firstly, we propose a robust and discriminative feature extraction method of different feature levels. The feature representations are projected to a lower computation common subspace. Secondly, we learn a single cross-view invariant dictionary for each feature level for different camera views and a fusion strategy is utilized to generate the final matching results. Experimental statistics show the superior performance of our approach by comparing with state-of-the-art methods on two publicly available benchmark datasets VIPeR and PRID 2011.

• 出版日期2018-5
• 单位上海交通大学