This paper presents a novel method for people counting in crowded scenes that combines the information gathered by multiple cameras to mitigate the problem of occlusion that commonly affects the performance of counting methods using single cameras. The proposed method detects the corner points associated to the people present in the scene and computes their motion vector. During the training step the mean number of points per person is estimated. The image plane is transformed to the ground plane using homography and weights are assigned to each corner point according to its distance to the camera since the farthest a person is from the camera, the less corner points are detected. The experimental results obtained on the benchmark PETS2009 video dataset show that proposed method surpasses other methods with improvements of up to 46.7% and provides accurate counting results for the crowded scenes.
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