Title :
Pedestrian detection based on combinational holistic and partial features
Author :
Yu, Chenglong ; Wang, Xuan
Author_Institution :
Comput. Applic. Res. Center, Harbin Inst. of Technol., Shenzhen, China
Abstract :
Pedestrian detection has been widely used in many applications, however, it is a challenging task and there are many problems unsolved to be handled. Althougth Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) is the most successful pedestrian detection algorithm, the detection rate is becoming worse when the portions of human partial ocllusions are increasing. We propose an approach of adding the head features based on HOG for improving pedestrian detection rates in the case of body partial occlusions. The experiment demonstrates that our approach is robust to the occlusions.
Keywords :
hidden feature removal; image enhancement; object detection; support vector machines; traffic engineering computing; combinational holistic; histograms of oriented gradients; human partial ocllusions; partial features; pedestrian detection algorithm; support vector machine; Computational modeling; Image color analysis; Image segmentation; Training; formatting; style; styling;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-1-4577-0305-8
DOI :
10.1109/ICMLC.2011.6016960