文献类型：会议论文

英文题名：Countour based HOG deer detection in thermal images for traffic safety

作者：Zhou, Debao[1]; Wang, Jingzhou[1]; Wang, Shufang[2]

第一作者：Zhou, Debao

通讯作者：Zhou, D.

机构：[1] Dept. of Mech. and Ind. Eng., University of Minnesota, Duluth, MN 55812, United States; [2] School of Mech. and Elec. Eng., Beijing Union University, Beijing, 100020, China

第一机构：Dept. of Mech. and Ind. Eng., University of Minnesota, Duluth, MN 55812, United States

通讯机构：[1]Dept. of Mech. and Ind. Eng., University of Minnesota, Duluth, MN 55812, United States

会议论文集：Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012

会议日期：July 16, 2012 - July 19, 2012

会议地点：Las Vegas, NV, United states

语种：英文

外文关键词：Automobile bodies - Computer vision - Infrared imaging - Rural roads - Support vector machines

摘要：Car accidents due to deer vehicle crashes (DVCs) are constantly a major safety issue for the driving on rural roads in the Europe and North America. Many attempts have been made to avoid these accidents, but few have been succeeded. One of the options is to use thermal images to identify the presence of deer. In this approach, large amount of image data have to be processed. This process will take long time and make the system incapable of real-time deer identification and tracking. Based on the well-known pattern recognition methods - histogram of orientated gradient (HOG) method and support vector machine method (SVM), this research developed a contour based HOG + SVM method, called CNT-HOG method. Experimental results showed that this algorithm has the advantages of both high accuracy (up to 95%) and less consumed time (0.1s) than traditional HOG+SVM method. Further analysis has been carried out to evaluate the influence of body postures and body blockages. By achieving this speed and accuracy, deer can be tracked in real-time. Accurate tracking and identification can then be achieved to reduce the possibility of deer - vehicle crashes.