Cost-Effective Vehicle Type Recognition in Surveillance Images With Deep Active Learning and Web Data

Recently, vehicle type recognition in surveillance images with deep learning has received significant attention in various applications of intelligent transportation systems. However, annotating large-scale images from many surveillance images is tedious and time-consuming, which impedes its application in the real world. This paper aims to resolve this problem by reducing manual labeling in surveillance images, and then maximizing the effect of the few tagged data. Thus, a deep active learning method with a new query strategy is proposed in this paper for vehicle type recognition in surveillance images. First, the proposed method constructs a memory space using a large-scale fully labeled auxiliary dataset collected from the Internet. Subsequently, two metrics, the similarity measurement in memory space and the entropy, are used to simultaneously emphasize the diversity and uncertainty in the query strategy. Moreover, an additional label-consistent term apart from the hyper-parameters is used to adaptively adjust the combination of the two principles in active learning. The proposed method was evaluated on the Comprehensive Cars dataset. The experimental results demonstrated that the proposed method could effectively reduce the annotation cost by up to 40% in surveillance vehicle type recognition compared with the random selection method.