A Neuromorphic Person Re-Identification Framework for Video Surveillance

This paper presents a neuromorphic person re-identification (NPReId) framework to establish the correspondence among individuals observed across two disjoint camera views. The proposed framework comprises three modules (observation, cognition, and contemplation), inspired by the form-and-color-and-depth (FACADE) theory model of object recognition system. In the observation module, a semantic partitioning scheme is introduced to segment a pedestrian into several logical parts, and an exhaustive set of experiments have been carried out to select the best possible complementary feature cues. In the cognition module, an unsupervised procedure is suggested to partition the gallery candidates into multiple consensus clusters with high intra-cluster and low inter-cluster similarity. A supervised classifier is then deployed to learn the relationship between each gallery candidate and its associated cluster, which is subsequently used to identify a set of inlier consensus clusters. This module also includes weighing of contribution of each feature channel toward defining a consensus cluster. Finally, in the contemplation module, the contributory weights are employed in a correlation-based similarity measure to find the corresponding match within the inlier set. The proposed framework is compared with several state-of-the-art methods on three challenging data sets: VIPeR, iLIDS-VID, and CUHK01. The experimental results, with respect to recognition rates, demonstrate that the proposed framework can obtain superior performance as compared with the counterparts. The proposed framework, along with its low-rank bound property, further establishes its suitability in practical scenarios through yielding high cluster hit rate with low database penetration.