Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features

Abstract

Noisy features may introduce irrelevant or incorrect features that can lead to incorrect classifications and lower accuracy. This can be especially problematic in tasks such as person re-identification (ReID), where subtle differences between individuals need to be accurately captured and distinguished. However, the existing ReID methods directly use noisy and limited multimodality features for similarity measures. It is crucial to use robust features and pre-processing techniques to reduce the effects of noise and ensure accurate classification. As a solution, we employ a Gaussian filter to eliminate the Gaussian noise from RGB-D data in the pre-processing stage. For similarity measure, the color descriptors are computed using the top eight peaks of the 2D histogram constructed from pose regularized partition grid cells, and eleven different skeleton distances are considered. The proposed method is evaluated on the BIWI RGBD-ID dataset, which comprises still (front view images) and walking set (images with varied pose and viewpoint) images. The obtained recognition rates of 99.15% and 94% on still and walking set images demonstrate the effectiveness of the proposed approach for the ReID task in the presence of pose and viewpoint variations. Further, the method is evaluated on and RGBD-ID and achieved improved performance over the existing techniques.