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

TL;DR

This paper introduces a scalable person re-identification approach that reduces annotation effort by using intra-camera labels only, employing a multi-task deep learning model to discover cross-camera identities and improve re-id performance.

Contribution

It proposes the Intra-Camera Supervised (ICS) setting and a novel Multi-Task Multi-Label (MTML) deep learning method for person re-id without inter-camera identity labels.

Findings

MTML outperforms state-of-the-art methods on large-scale datasets.

The approach significantly reduces annotation effort.

Effective cross-camera identity discovery is achieved without explicit labels.

Abstract

Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications. To overcome this fundamental limitation, we consider person re-identification without inter-camera identity association but only with identity labels independently annotated within each individual camera-view. This eliminates the most time-consuming and tedious inter-camera identity labelling process in order to significantly reduce the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which we call Intra-Camera Supervised (ICS) person re-id. Under this ICS setting with weaker label supervision, we formulate a Multi-Task Multi-Label (MTML) deep learning method. Given no inter-camera association, MTML is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MTML can also efficiently learn the discriminative re-id feature representations by fully using the available identity labels within each camera-view. Extensive experiments demonstrate the performance superiority of our MTML model over the state-of-the-art alternative methods on three large-scale person re-id datasets in the proposed intra-camera supervised learning setting.