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Pedestrian detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. To continue the rapid rate of innovation, we introduce the Caltech Pedestrian Dataset, which is two orders of magnitude larger than existing datasets...
We present a comparative evaluation of the state-of-art algorithms for detecting pedestrians in low frame rate and low resolution footage acquired by mobile sensors. Four approaches are compared: a) The Histogram of Oriented Gradient (HoG) approach; b) A new histogram feature that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian...
In this paper, we propose a facial direction estimating system from low resolution facial images captured by surveillance camera. The proposed system first detects pedestrians by background subtraction, then trimming head area by template matching and finally estimating facial direction by using support vector machine (SVM). Experimental results show that, when we use 8times9 pixels facial images...
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