A NOVEL BIOMECHANICS-BASED APPROACH FOR PERSON RE-IDENTIFICATION BY GENERATING DENSE COLOR SIFT SALIENCE FEATURES

Currently, identifying humans using biomechanics-based approaches has gained a lot of significance for person re-identification. Biomechanics-based approaches use knee-hip angle-angle relationships and body movements for person re-identification. Generally, biomechanics of human walking and running is used for person re-identification. In fact, person re-identification is a complex and important task in academia as well as industry and remains an unsolved issue in the computer vision field. The subjects most commonly addressed regarding person re-identification include significant feature extraction that can function accurately with invariant appearance and robust classification. In this study, a significant color feature descriptor is proposed by combining dense color-SIFT and global convex hull salience region features. First convex hull boundary points are detected using the SIFT technique. Furthermore, it is extended with Grubb's outlier test to eliminate the outlier points detected by SIFT and mark the saliency region via convex hull. Then dense-SIFT and dense-CHF methods are used to extract local and global features within the convex hull region, respectively. Finally, the pre-ranked common nearest neighbor selection technique is applied to minimize overhead of dataset and generate more robust rank classification. The proposed technique is tested using three-camera database video sequences and three publicly available datasets, namely i-LIDS, VIPeR and GRID. Performance of re-identification system is evaluated using a statistical method with CMC curves. The results show better re-identification accuracy in solving the aforementioned problems.