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

Xiangping Zhu, Xiatian Zhu, Minxian Li, Vittorio Murino, Shaogang Gong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0


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 labeling 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.

Related Material

author = {Zhu, Xiangping and Zhu, Xiatian and Li, Minxian and Murino, Vittorio and Gong, Shaogang},
title = {Intra-Camera Supervised Person Re-Identification: A New Benchmark},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}