Learning a Repression Network for Precise Vehicle Search
This addresses the challenge of distinguishing similar vehicles in surveillance systems for public security, representing an incremental improvement with specific gains in efficiency.
The paper tackles the problem of precise vehicle search from large-scale image databases by proposing a Repression Network (RepNet) that learns discriminative features from coarse and detailed levels, achieving state-of-the-art performance on the revised VehicleID dataset and reducing retrieval time by about 24 times with a bucket search method.
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 detailed 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 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.