Intra-Camera Supervised Person Re-Identification: A New Benchmark

Abstract

Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person reidentification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness Camera 3 Camera 1 Camera 4 Camera 2 (a) (b) Fig. 1 (a) Person re-identification challenges. Each triplet bounded by a dashed box shows the images of a single person from different camera views. (b) Illustration of manually associating identities across camera-views. The dashed arrow denotes the comparison between two identities. The associated identities are bounded with red boxes.superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors.

Intra-Camera Supervised Person Re-Identification

“…A preliminary version of this work was published in Zhu et al (2019b). Compared with this earlier study, there are a number of key differences: (i) This study presents a more comprehensive investigation into the proposed ICS person re-id paradigm in terms of training data annotation complexity, along with a comparison to the standard cross-camera identity labelling method.…”

Graph-Induced Contrastive Learning for Intra-Camera Supervised Person Re-Identification

“…This work aims to learn a person Re-ID model under intracamera supervision (ICS), which is a new semi-supervised setting proposed very recently [16], [17]. It assumes that identity labels are independently annotated within each cam-era view and no inter-camera identity association is labeled.…”

“…• We propose a graph partitioning based strategy for ID association in ICS. In contrast to previous ICS works that use soft-labeling [17] or multi-label learning [16], [22] strategies, our association strategy can ensure the ICS-specific constraints to be satisfied so that more reliable association results are obtained. • We propose a graph-induced contrastive learning method to learn the Re-ID model.…”

“…• We propose a graph-induced contrastive learning method to learn the Re-ID model. In contrast to previous ICS works [16], [17], [22], [23] that treat intraand inter-camera learning differently and separately, our approach unifies both learning parts into the same framework and performs them end-to-end, enabling the learning more effective.…”

“…To ensure these assumptions, both supervisions involve inter-camera associations or comparisons. Recently, intra-camera supervision (ICS) [16] is proposed as a new and special semi-supervised setting. It provides only intra-camera labels, which may be automatically obtained by tracking algorithms, and leaves out the time-consuming intercamera associations.…”