A robust pedestrian detection approach based on shapelet feature and Haar detector ensembles

2012 ◽  
Vol 17 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Wentao Yao ◽  
Zhidong Deng
Keyword(s):  
Pedestrian Detection ◽  
Detection Approach

A Novel Fast Pedestrian Detection Method

2015 ◽  
Vol 738-739 ◽  
pp. 538-541
Author(s):  
Fu Qiang Zhou ◽  
Yan Li
Keyword(s):  
Video Streaming ◽  
Local Binary Pattern ◽  
Detection Method ◽  
Pedestrian Detection ◽  
Original Method ◽  
Histograms Of Oriented Gradients ◽  
Detection Approach

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

scholarly journals Pedestrian Detection Approach for Driver Assisted System using Haar based Cascade Classifiers

2018 ◽  
Vol 9 (6) ◽  
Author(s):  
M. Ameen Chhajro ◽  
Kamlesh Kumar ◽  
M. Malook ◽  
Aftab Ahmed ◽  
Haque Nawaz ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Cascade Classifiers ◽  
Detection Approach

scholarly journals An Efficient Pedestrian Detection Approach Using a Novel Split Function of Hough Forests

2014 ◽  
Vol 8 (4) ◽  
pp. 207-214 ◽  
Author(s):  
Trung Dung Do ◽  
Thi Ly Vu ◽  
Van Huan Nguyen ◽  
Hakil Kim ◽  
Chongho Lee
Keyword(s):  
Pedestrian Detection ◽  
Detection Approach ◽  
Split Function ◽  
Hough Forests

scholarly journals Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 780 ◽  
Author(s):  
Chao Zhu ◽  
Xu-Cheng Yin
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution ◽  
Specific Test ◽  
Adaboost Algorithm ◽  
Detection Approach ◽  
Major Bottleneck ◽  
Boosting Algorithm ◽  
Test Sets ◽  
Late Stages ◽  
Possible Cause

Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.

A Real Time Object Detection Approach Applied to Reliable Pedestrian Detection

2007 ◽  
Author(s):  
Guanglin Ma ◽  
Su-Birm Park ◽  
Alexander Ioffe ◽  
Stefan Muller-Schneiders ◽  
Anton Kummert
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Pedestrian Detection ◽  
Detection Approach
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