|Table of Contents|

Cascade AdaBoost pedestrian detector with multi-features and multi-thresholds(PDF)

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

Issue:
2015年02期
Page:
109-117
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Cascade AdaBoost pedestrian detector with multi-features and multi-thresholds
Author(s):
CUI Hua;  ZHANG Xiao;  GUO Lu;  YUAN Chao;  XUE Shi-jiao;  SONG Huan-sheng
School of Information Engineering, Chang’an University, Xi’an 710064, Shaanxi, China
Keywords:
traffic image processing;  pedestrian detection;  feature extraction;  AdaBoost classifier;  region division;  cascade rule
PACS:
U491.6
DOI:
-
Abstract:
In order to meet the practical demand for pedestrian detection with high speed, high accuracy and strong robustness, in view of the poor quality and unapparent local image features of traffic videos, some simple pedestrian features were chosen for pedestrian detection. Besides rectangle degree, ratio of height to width, shape complexity, normalized width, and pedestrian area, head density was applied because it is a simple local feature and has strong robustness for occlusion interference. Considering the size changing of pedestrian in the image, region division strategy was introduced into image region division. An improved training algorithm based on the minimum principle of classification error and the maximum principle of positive sample classification rate was implemented by considering both high detection rate and low false detection rate, thus several single-feature AdaBoost pedestrian detectors with multi-thresholds were obtained. To optimize the detection performance of cascade pedestrian detectors, the cascade rule was obtained in term of the contribution rate. The contribution rate was defined by weighing detection performance and detection speed. The cascade order was the detectors based on ratio of height to width, normalized width, rectangle degree, pedestrian area, shape complexity and head density. The pedestrian detectors were sequentially cascaded according to the cascade order, thus a cascade AdaBoost pedestrian detector with multi-features and multi-thresholds was constructed. The proposed pedestrian detector was tested by using 3 traffic scenes, and compared with single-cascade-level AdaBoost pedestrian detector and 2 existed cascade AdaBoost pedestrian detectors. Analysis result indicates that in 3 traffic scenes, compared with the other pedestrain detectors, the proposed pedestrain detector has higher detection rate, higher detection speed and lower false detection rate, the minimum detection rate is 96.70%, the maximum false detection rate is 0.67%, and the detection time is less than 5 s. So the detector satisfies the real-time and reliable requirements of pedestrian detection in traffic scene. 1 tab, 5 figs, 24 refs.

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Last Update: 2015-04-30