Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio

Hand-Crafted System for Person Re-Identification:A Comprehensive Review

International audienceIn video surveillance, Person Re-Identification(Re-ID) consists in recognizing an individual who has already been observed (hence the term Re-Identification) over a network of cameras. Usually, the person Re-Id system is divided into two stages: i)constructing a person's appearance signature by extracting feature representations which should be robust against pose variations, illumination changes and occlusions and ii)Establishing the correspondence/matching between feature representations of probe and gallery by learning similarity metrics or ranking functions. A gallery is a dataset composed of images of people with known IDs whereas a probe is collected of detected persons with unknown IDs from different cameras. Specifically, the process of person Re-Identification aims essentially at matching individuals across non-overlapping cameras at different instants and locations. However, the matching is challenging due to disparities of human bodies and visual ambiguities across different cameras. This paper provides an overview of hand-crafted system for person Re-identification, including features extraction and metric learning as well as their advantages and drawbacks. The performance of some state-of-the-art person Re-ID methods on the commonly used benchmark datasets is compared and analyzed. It also provides a starting point for researchers who want to conduct novel investigations on this challenging topic