Real-Time Pedestrian Detection and Tracking
نویسندگان
چکیده
Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact the quality of life. This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection and tracking in Intelligent Systems based on monocular vision. First, we detect the pedestrian using Integral Channel Features and AdaBoost classifier, which is implemented with Modified Soft Cascade to achieve robust thresholds. Later we track the pedestrian for the next few frames based on Lucas Kanade features. The experiment results show that the method can detect and track pedestrian ahead of vehicle in spite of different sizes and postures. The algorithms have been tested on Inria database for the detection system and Caltech and Daimler datasets for the detection and tracking system.
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