Title :
A Pedestrian Detection and Tracking System Based on Video Processing Technology
Author :
Yuanyuan Chen ; Shuqin Guo ; Biaobiao Zhang ; Du, K.-L.
Author_Institution :
Enjoyor Labs., Enjoyor Inc., Hangzhou, China
Abstract :
Pedestrian detection and tracking are widely applied to intelligent video surveillance, intelligent transportation, automotive autonomous driving or driving-assistance systems. We select OpenCV as the development tool for implementation of pedestrian detection, tracking, counting and risk warning in a video segment. We introduce a low-dimensional soft-output SVM pedestrian classifier to implement precise pedestrian detection. Experiments indicate that the system has high recognition accuracy, and can operate in real time.
Keywords :
image classification; image sequences; object tracking; pedestrians; support vector machines; video signal processing; OpenCV development tool; automotive autonomous driving; driving-assistance systems; intelligent transportation; intelligent video surveillance; low-dimensional soft-output SVM pedestrian classifier; pedestrian counting; pedestrian detection and tracking system; pedestrian risk warning; video processing technology; video segment; Accuracy; Feature extraction; Gaussian mixture model; Histograms; Support vector machines; Trajectory; pedestr-ian counting; pedestrian detection; pedestrian tracking; risk warning;
Conference_Titel :
Intelligent Systems (GCIS), 2013 Fourth Global Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4799-2885-9
DOI :
10.1109/GCIS.2013.17
