Huang Kaiqi

Image enhancement based on the statistics of visual representation

This paper introduces a novel algorithm to image enhancement that exploits the multi-scale wavelet and statistical characters of visual representation. Processing includes the global dynamic range (brightness) correction and local contrast adjustment, whose parameters are picked automatically by the information contained in the image itself. Experimental results show that the new algorithm outperforms other many existing image enhancement methods and is highly resilient to the effects of both the image-source variations

Video-based crowd density estimation and prediction system for wide-area surveillance

Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowds density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.