Predominant color name indexing structure for person re-identification
The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from part-based models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.