A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
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Efficiently and accurately detecting pedestrian plays a very important role in many computer vision applications such as Intelligent Transportation System and Safety Driving Assistant. This paper puts forwards a two-stage pedestrian detection method based on machine vision. Firstly, the expanded Haar-like characteristic is selected and calculated using integral map and the pedestrian detection cascaded classifiers with high accuracy are trained by Adaboost. After segmenting the candidate pedestrian areas from the image, a confirmation step is needed to judge whether those areas are pedestrian or not. Through analyzing the sample images, we can know that the gray image of pedestrian has some texture and gray symmetry features. In addition, the continuous edges of pedestrian make the extracted edges have certain boundary moments and gradient direction characters. Based on these features, each sample image is expressed by a multidimension characteristic vector. The final pedestrian classifier is obtained using support vector machines (SVM) training with the features abstracted above. The experiment results indicate that the algorithm could achieve effective recognition of vehicle proceeding pedestrians with different sizes, colors and shapes.