Is Pedestrian Detection Really a Hard Task?∗

In this paper we present a simple approach for person detection in surveillance for static cameras. The basic idea is to train a separate classifier for each image location which has only to discriminate the object from the background at a specific location. This is a considerably simpler problem than the detection of persons on arbitrary backgrounds. Therefore, we use adaptive classifiers which are trained online. Due to the reduced complexity we can use a simple update strategy that requires only a few positive samples and is stable by design. This is an essential property for real world applications which require operation for 24 hours a day, 7 days a week. We demonstrate and evaluate the method on publicly available sequences and compare it to state-of-theart methods which reveals that despite the simple strategy the obtained performance is competitive.