Randomised forests for people detection

People detection is an important task with applications in fields such as surveillance and human computer interaction. A popular approach to this problem is to train a classifier on a data set using a particular set of features. A great deal of empirical evidence suggests that edge features are particularly discriminative for this task. In this paper we explore the use of randomised forests (sometimes referred to as randomised decision forests) for people detection. A randomised forest classifier is trained for people detection with edge orientation features. These features capture information concerning the distribution of edges with specific orientations. The classifier is trained and tested on the INRIA person data set, and some results are presented.