Scene specific people detection by simple human interaction

This paper proposes a generic procedure for training a scene specific people detector by exploiting simple human interaction. This technique works for any kind of scene imaged by a static camera and allows to considerably increase the performances of an appearance-based people detector. The user is requested to validate the results of a basic detector relying on background subtraction and proportions constraints. From this simple supervision it is possible to select new scene specific examples that can be used for retraining the people detector used in the testing phase. These new examples have the benefit of adapting the classifier to the particular scene imaged by the camera, improving the detection for that particular viewpoint, background, and image resolution. At the same time, positions and scales, where people can be found, are learnt, thus allowing to considerably reduce the number of windows that have to be scanned in the detection phase. Experimental results are presented on three different scenarios, showing an improved detection accuracy and a reduced number of false positives even when the ground plane assumption does not hold.