People re-identification under occlusion and crowded background

The performance of video surveillance systems with network cameras depends on their accuracy in people re-identification. Body occlusion, crowded background, and variations in scene illumination and pose are challenging issues in people re-identification. In this paper, a technique is proposed to improve the performance of re-identification approaches using (a) a pre-processing step; and (b) a proposed weighing mechanism. In this approach, first, the input image is segmented into the person's body, background, and possible carried objects. Then, considering the image's segments, the occluded parts of the body are retrieved using their neighboring pixels. The processed image is transformed into the log chromatically color space which is robust to scene illumination changes. Using the transformed images along with descriptors which are robust to appearance changes such as Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptor (HGD) can improve performance of the descriptors. In this paper, the GOG and HGD are used in a weighed form to represent the pre-processed images considering the importance of each segment of the images in people re-identification. The proposed re-identification system is evaluated using VIPeR and PRID450s datasets, where it respectively achieves 61.9% and 83.4% rank-1 matching rates. Experimental results show that our proposed approach outperforms other existing approaches in people re-identification.

Automated summary

This paper proposes a technique to improve the performance of re-identification approaches using pre-processing and weighing mechanism. The approach involves segmenting the input image, retrieving occluded body parts, transforming the processed image, and using robust descriptors. The proposed system achieves high matching rates on VIPeR and PRID450s datasets, outperforming existing approaches.