Fast pedestrian detection using a cascade of boosted covariance features,” Circuits and Systems for Video Technology

Abstract—Efficiently and accurately detecting pedestrians plays a very important role in many computer vision applications such as video surveillance and smart cars. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally extracted fea-tures (e.g., local receptive fields, histogram of oriented gradients, and region covariance). Building upon the findings of our exper-iments, we propose a new, simpler pedestrian detector using the covariance features. Unlike the work in [1], where the feature selec-tion and weak classifier training are performed on the Riemannian manifold, we select features and train weak classifiers in the Eu-clidean space for faster computation. To this end, AdaBoost with weighted Fisher linear discriminant analysis-based weak classi-fiers are designed. A cascaded classifier structure is constructed for efficiency in the detection phase. Experiments on different datasets prove that the new pedestrian detector is not only comparable to the state-of-the-art pedestrian detectors but it also performs at a faster speed. To further accelerate the detection, we adopt a faster strategy—multiple layer boosting with heterogeneous features—to exploit the efficiency of the Haar feature and the discriminative power of the covariance feature. Experiments show that, by com-bining the Haar and covariance features, we speed up the original covariance feature detector [1] by up to an order of magnitude in detection time with a slight drop in detection performance. Index Terms—AdaBoost, boosting with heterogeneous features, local features, pedestrian detection/classification, support vector machine. I