Learning a Repression Network for Precise Vehicle Search


The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from large-scale image databases. Precise vehicle search, aiming at finding out all instances for a given query vehicle image, is a challenging task as different vehicles will look very similar to each other if they share same visual attributes. To address this problem, we propose the Repression Network (RepNet), a novel multi-task learning framework, to learn discriminative features for each vehicle image from both coarse-grained and fine-grained level simultaneously. Besides, benefited from the satisfactory accuracy of attribute classification, a bucket search method is proposed to reduce the retrieval time while still maintaining competitive performance. We conduct extensive experiments on the revised VehcileID [1] dataset. Experimental results show that our RepNet achieves the state-of-the-art performance and the bucket search method can reduce the retrieval time by about 24 times.

Cite this paper

@article{Xu2017LearningAR, title={Learning a Repression Network for Precise Vehicle Search}, author={Qiantong Xu and Ke Yan and YongHong Tian}, journal={CoRR}, year={2017}, volume={abs/1708.02386} }