# Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes Source: [Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:24:37.002Z Authors: Wenhang Ge, Chunyan Pan, Ancong Wu, Hongwei Zheng, Wei-Shi Zheng Published: 2021-07-29T11:27:50.000Z ## Abstract Person re-identification (Re-ID) aims to match person images across non-overlapping camera views. The majority of Re-ID methods focus on small-scale surveillance systems in which each pedestrian is captured in different camera views of adjacent scenes. However, in large-scale surveillance systems that cover larger areas, it is required to track a pedestrian of interest across distant scenes (e.g., a criminal suspect escapes from one city to another). Since most pedestrians appear in limited local areas, it is difficult to collect training data with cross-camera pairs of the same person. In this work, we study intra-camera supervised person re-identification across distant scenes (ICS-DS Re-ID), which uses cross-camera unpaired data with intra-camera identity labels for training. It is challenging as cross-camera paired data plays a crucial role for learning camera-invariant features in most existing Re-ID methods. To learn camera-invariant representation from cross-camera unpaired training data, we propose a cross-camera feature prediction method to mine cross-camera self supervision information from camera-specific feature distribution by transforming fake cross-camera positive feature pairs and minimize the distances of the fake pairs. Furthermore, we automatically localize and extract local-level feature by a transformer. Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme for mining fine-grained cross-camera self supervision information. Finally, cross-camera self supervision and intra-camera supervision are aggregated in a framework. The experiments are conducted in the ICS-DS setting on Market-SCT, Duke-SCT and MSMT17-SCT datasets. The evaluation results demonstrate the superiority of our method, which gains significant improvements of 15.4 Rank-1 and 22.3 mAP on Market-SCT as compared to the second best method. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Person Re-identification on MSMT-SCT](): Person Re-Identification; MSMT-SCT; R-1: 54.9. - [Person Re-identification on Market-SCT](): Person Re-Identification; Market-SCT; Rank-1 Accuracy (R-1): 84.1. ## Original sources and implementation [Original paper]() [PDF]()