ST-CSNN: a novel method for vehicle counting

Vehicle counting using computer vision techniques has potential to alleviate traffic congestion in intelligent transportation system. In this paper, we propose a novel method to count vehicles in a human-like manner. This paper has two main contributions. Firstly, we propose ST-CSNN, which is an efficient, lightweight vehicle counting method. The method counts based on vehicle identity comparison to omit duplicate instances. Combined with the spatio-temporal information between frames, it is able to accelerate speed and improve accuracy of counting. Secondly, we strengthen the method’s performance by proposing an improved loss function on the basis of Siamese neural network. Besides, we conduct experiments on several datasets to evaluate the performance of the proposed loss function for verification and the whole method for counting. The experimental results show the practicability of this method for real counting scenes.