Neural Information Processing

Real-time crowd counting is of many potential applications, such as surveillance, crowd flow control in subway. In this paper, we propose a fast and novel method for estimating the number of people in crowded surveillance scenes. This method is able to count people in real time and is robust to changes of illumination and background. The combined rectangle features and cascade of boosted classifier are employed to train a multi-scale head-shoulder detector. The detector can detect human in every frame with a high accuracy. Then human tracking is used to track the detected people and remove duplicates in successive frames. Experiments on a real-world video show the proposed method can give an accurate estimation in real time.

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