Abstract
In this Paper, Propose a Pedestrian Detection Method that Based on Adaboost Algorithm and Pedestrian Shape Features Integration. First According to the Collected Pedestrian True, False Sample, Selected the Characteristics of the Extended Class Haar, Adopt Adaboost Algorithm Training Get Pedestrian Classifier to Split the Initial Candidate Region of All Pedestrians in the Image. in this Paper, Propose an Adaptive Threshold Weight Update Method, Significantly Reduced the Number of the Characteristics of Strong Classifier, Optimize the Classifier Structure, Reduce the Complexity of the Algorithm; Meanwhile, the Online Update Detector, Improving the Reliability of the Detector. Pedestrian Leg Have Strong Vertical Edge Symmetry Characteristic so that Extracted the Vertical Edge Detection in the Initial Candidate Region, According to the Symmetry Determine the Vertical Axis of Symmetry, Combined with the Morphological Characteristics of Pedestrians to Determine the Width and Height Characteristics of the Pedestrian, to Determine the Pedestrian Candidate Region, Finally, Put a Further Validation to the Pedestrian Candidate Region.
Highlights
With China's rapid increase in car ownership, frequency of road traffic accidents, especially the traffic accidents caused by vehicle and pedestrian collision, is the main reason for pedestrian casualties
This paper presents a pedestrian candidate region segmentation method based on the integration of the shape characteristics of the pedestrian and AdaBoost algorithm, extract the possible position of the pedestrians in the image, so that provide input for the effective identification of pedestrian.[1,2]
As the Information of visual sensor is rich, according to pedestrians shape characteristics and AdaBoost algorithm features, in this paper, we propose a pedestrian segmentation method which based on the Integration of pedestrians shape characteristics and AdaBoost algorithm
Summary
With China's rapid increase in car ownership, frequency of road traffic accidents, especially the traffic accidents caused by vehicle and pedestrian collision, is the main reason for pedestrian casualties. This extended sample weights update algorithm can reduce the FNR and effectively limit the increase of the overall sample error rate but it can not control the degree of FNR reduced. The simulation results show that the threshold adaptive sample weights update method can rapidly reduce the value of FNR in less rounds of training in the case of ensuring the overall error rate unchanged. We use the class of Haar feature here; this rectangular feature is more sensitive to the edge and the line segments and the Eigen value of the class Haar is calculated very quickly.[10] The number of sample Features. Each class Haar feature is composed of 2 to 4 rectangles which are used to detect edges, linear features and diagonal features The calculation of these features is the sum of the gray Integration of the rectangular area. You can find a way to quickly calculate each Haar features just like the way to lookup tables. [4]
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