Multi-Resolution Overlapping Stripes Network for Person Re-Identification
This work addresses person re-identification for surveillance and security applications, but it appears incremental as it builds on existing part-based and global feature approaches.
The paper tackles the person re-identification problem by developing a part-based model that integrates global and local information at multiple feature resolutions using overlapping stripes and different loss functions, and it outperforms state-of-the-art methods on a benchmark dataset.
This paper addresses the person re-identification (PReID) problem by combining global and local information at multiple feature resolutions with different loss functions. Many previous studies address this problem using either part-based features or global features. In case of part-based representation, the spatial correlation between these parts is not considered, while global-based representation are not sensitive to spatial variations. This paper presents a part-based model with a multi-resolution network that uses different level of features. The output of the last two conv blocks is then partitioned horizontally and processed in pairs with overlapping stripes to cover the important information that might lie between parts. We use different loss functions to combine local and global information for classification. Experimental results on a benchmark dataset demonstrate that the presented method outperforms the state-of-the-art methods.