|Table of Contents|

Model of real-time pedestrian detection under vehicle environment based on CS-SD(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2016年06期
Page:
132-139
Research Field:
交通信息工程及控制
Publishing date:
2016-12-20

Info

Title:
Model of real-time pedestrian detection under vehicle environment based on CS-SD
Author(s):
GUO Ai-ying1; 2;  XU Mei-hua1;  RAN Feng3;  WANG Qi3
1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China; 2. Department of Mechatronic Engineering, Shanxi Vocational and Technical College of Light Industry, Taiyuan 030013, Shanxi, China; 3. Microelectronics Research and Development Center,Shanghai University, Shanghai 200072, China
Keywords:
pedestrian detection;  histogram of oriented gradient;  pedestrian area;  feature extraction;  vehicle equipment
PACS:
U491.6
DOI:
-
Abstract:
In order to solve the real-time problem in the advanced driver assistant system, a model of pedestrian detection based on the calibration of side-of-pavement line and saliency texture detection(CS-SD)and the location histogram of oriented gradient(L-HOG)was proposed. The CS-SD algorithm was used instead of exhaustive search to quickly mark pedestrian area in the image. The L-HOG was used to quickly extract pedestrian feature, and additive kernel support vector machine(AK-SVM)was used to efficiently classify detected objects. Analysis result shows that when 500 images including 832 pedestrians on personal computer are detected, the model detects 720 pedestrians correctly, the detection rate is 86.5%, the error rate is 4.1%, and the detection time is 39 ms. When 48 400 images including 988 pedestrians on vehicle pedestrian detection system based on BF609 are detected, the model detects 861 pedestrians correctly, misses 127 pedestrians and detects 13 pedestrians in error. The detection speed is 20 fps. Under the premise of not reducing the detection rate, the proposed pedestrian detection model can reach satisfying detection speed and can be used in vehicle equipment of real-time pedestrian detection. 4 tabs, 8 figs, 25 refs.

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Last Update: 2016-12-20