Cross-View Contextual Relation Transferred Network for Unsupervised Vehicle Tracking in Drone Videos

Wenfeng Song, Shuai Li, Tao Chang, Aimin Hao, Qinping Zhao, Hong Qin; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1707-1716

Abstract


Recently CNN-centric object tracking methods have been gaining tremendous success in ground-view videos, however, it remains hard to cope with vehicle tracking in unmanned aerial vehicle (UAV) videos. The key difficulties mainly stem from lacking large-scale well-labeled training datasets and view-invariant appearance model for fast-moving drone-view vehicles. We enhance the vehicle's cross-view feature by exploring relations between the pivotal context and the target to facilitate unsupervised vehicle tracking. The relation is modeled as the relevance of the target and its contextual regions in the tracking task. Specifically, we propose a contextual relation actor-critic (CRAC) framework integrates an actor-critic agent with a dual GAN learning mechanism, which aims to dynamically search the related contextual regions and transfer the relations from ground-view to drone-view videos while retaining the discriminative features. We demonstrate that CRAC could be applied to several state-of-the-art trackers by extensive experiments and ablation studies on four public benchmarks. All the experiments confirm that, our CRAC can improve the performance of state-of-the-art methods in terms of accuracy, robustness, and versatility.

Related Material


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[bibtex]
@InProceedings{Song_2020_WACV,
author = {Song, Wenfeng and Li, Shuai and Chang, Tao and Hao, Aimin and Zhao, Qinping and Qin, Hong},
title = {Cross-View Contextual Relation Transferred Network for Unsupervised Vehicle Tracking in Drone Videos},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}