Unsupervised learning of human perspective context using ME-DT for efficient human detection in surveillance

A novel and automated technique for learning human perspective context (HPC) from a scene is proposed in this paper. It is found that two models are required to describe HPC for camera tilt angle ranging from 0deg to 50deg. From a scene, the tilt angle can be inferred from the observed human shapes and head/foot positions. Afterward, a novel ME-DT (model estimation - data tuning) algorithm is proposed to learn human perspective context from live data of various degrees of uncertainties. The uncertainties may come from the variations of human individual heights and poses, and segmentation/recognition errors. ME-DT not only estimates the model parameters from the training data but also tunes the data to achieve a better head-foot correlation. The human perspective context provides a feasible constraint on the scales, positions, and orientations of humans in the scene. Applying this constraint to the HOG human detection, great reduction of the detection windows and improved performances have been obtained compared to conventional methods.