Crowd Counting for Real Monitoring Scene

Crowd counting is a challenging task in computer vision as realistic scenes are always filled with unfavourable factors such as severe occlusions, perspective distortions and diverse distributions. Recent state-of-the-art methods based on convolutional neural network(CNN) weaken these factors via multi-scale feature fusion or optimal feature selection through a front switch-net. L2 regression is used to regress the density map of the crowd, which is known to lead to an average and blurry result, and affects the accuracy of crowd count and position distribution. To tackle these problems, we take full advantage of the application of generative adversarial networks(GANs) in image generation and propose a novel crowd counting model based on conditional GANs to predict high-quality density maps from crowd images.Furthermore, we innovatively put forward a new regularizer so as to help boost the accuracy of processing extremely crowded scenes. Extensive experiments on four major crowd counting datasets are conducted to demonstrate the better performance of the proposed approach com-pared with recent state-of-the-art methods.