Body Parts Features-Based Pedestrian Detection for Active Pedestrian Protection System

Abstract

A novel pedestrian detection system based on vision in

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

“…In the state-of-the-art approaches, most researchers have been working on two issues: feature extraction methods (handcrafted features and deep convolutional features) and classification through the machine learning algorithms. Some of the promising handcrafted feature extraction methods are the histogram of oriented gradients (HOG) [4-6, 14, 24, 25], Haar wavelet [2,[25][26][27], scale-invariant feature transform (SIFT) [28][29][30], edge templates [5,23,31,32], adaptive contour features [2,23,33,34], Gabor filters [15,27], covariance descriptors [11,15,19,35], and local binary pattern (LBP) [6,11,24,36]. Among these handcrafted features, the histogram of oriented gradients (HOG) is a well-known feature descriptor for pedestrian detection due to the rich feature information under different illumination changes [14,16,20].…”

“…In the classification method, the well-known techniques such as support vector machine (linear and latent) [4,5,10,12,25,38], AdaBoost [7,9,34,39], neural network [2,3], random forest [3], and cascade [7,17,27] had been widely used. Among them, the linear SVM is one of the useful techniques that can compute faster than a latent SVM in terms of performance and efficiency.…”

Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment

“…However, most of those methods are more complex and easily disturbed by surrounding noise environment. The main features for pedestrian detection include Haar [5], the histogram of oriented gradient (HOG) [6], scaleinvariant feature transform [7], local self-similarity [8], local binary patterns (LBP) [9], and covariance features [10] currently. Ge et al [11] proposed a night-time pedestrian detection method based on Haar and HOG features.…”