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 im- aged 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 detec- tor relying on background subtraction and proportions con- straints. 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 ex- amples have the benefit of adapting the classifier to the par- ticular scene imaged by the camera, improving the detec- tion 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 consider- ably 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.