Person Detection for Indoor Videosurveillance Using Spatio-temporal Integral Features

In this paper, we address the problem of person detection in indoor videosurveillance data. We present a new method based on the state of the art integral channel features. This approach is extended to allow the use of temporal features in addition to appearance based features. The temporal features are integrated by a robust background subtraction method. Our method is then evaluated on several datasets presenting various and challenging conditions typical of videosurveillance context. The evaluation shows that additional temporal features are efficient and improve greatly the performance of the detector.