SLBP: An Improved Texture Feature for Pedestrian Detection

2017 ◽  
Author(s):  
Tan Feigang
Keyword(s):  
Pedestrian Detection ◽  
Texture Feature

scholarly journals Pedestrian detection based on gradient and texture feature integration

2017 ◽  
Vol 228 ◽  
pp. 71-78 ◽  
Author(s):  
Chun-Hou Zheng ◽  
Wen-Juan Pei ◽  
Qing Yan ◽  
Yan-Wen Chong
Keyword(s):  
Pedestrian Detection ◽  
Texture Feature ◽  
Feature Integration

scholarly journals Pedestrian Detection under Parallel Feature Fusion Based on Choquet Integral

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 250
Author(s):  
Rong Yang ◽  
Yun Wang ◽  
Ying Xu ◽  
Li Qiu ◽  
Qiang Li
Keyword(s):  
Feature Fusion ◽  
Choquet Integral ◽  
Detection System ◽  
Pedestrian Detection ◽  
Texture Feature ◽  
Current Method ◽  
Detection Accuracy ◽  
Parallel Feature ◽  
New Feature ◽  
The Cost

Feature-based pedestrian detection method is currently the mainstream direction to solve the problem of pedestrian detection. In this kind of method, whether the appropriate feature can be extracted is the key to the comprehensive performance of the whole pedestrian detection system. It is believed that the appearance of a pedestrian can be better captured by the combination of edge/local shape feature and texture feature. In this field, the current method is to simply concatenate HOG (histogram of oriented gradient) features and LBP (local binary pattern) features extracted from an image to produce a new feature with large dimension. This kind of method achieves better performance at the cost of increasing the number of features. In this paper, Choquet integral based on the signed fuzzy measure is introduced to fuse HOG and LBP descriptors in parallel that is expected to improve accuracy without increasing feature dimensions. The parameters needed in the whole fusion process are optimized by a training algorithm based on genetic algorithm. This architecture has three advantages. Firstly, because the fusion of HOG and LBP features is parallel, the dimensions of the new features are not increased. Secondly, the speed of feature fusion is fast, thus reducing the time of pedestrian detection. Thirdly, the new features after fusion have the advantages of HOG and LBP features, which is helpful to improve the detection accuracy. The series of experimentation with the architecture proposed in this paper reaches promising and satisfactory results.

Haar-Like Multi-Granularity Texture Features for Pedestrian Detection

2017 ◽  
Vol 17 (04) ◽  
pp. 1750023 ◽  
Author(s):  
Ruzhong Cheng ◽  
Yongjun Zhang ◽  
Guoping Wang ◽  
Yong Zhao ◽  
Rahmatulloev Khusravsho
Keyword(s):  
Human Body ◽  
Pedestrian Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Surveillance Systems ◽  
Robust Detection ◽  
Driver Support Systems ◽  
Haar Feature ◽  
Feature Dimension ◽  
Self Driving Cars

Pedestrian detection has been a significant problem for decades and remains a hot topic in computer vision. Pedestrian detection is one of the key algorithms for self-driving cars and some other functions in robotics, including driver support systems, road surveillance systems. In this paper, based on the characteristics of the human body and the Haar feature, the Haar-like multi-granularity local texture feature, i.e., multi-granularity Haar-like LBP (mgh-LBP), is proposed for pedestrian detection. The mgh-LBP feature combines four characteristics of the human body and their backgrounds to construct the Haar-like features, which can better describe human body texture and edge information. Compared with other texture features, including the rotation-invariant LBP feature, uniform LBP feature and basic-LBP feature, the proposed method greatly reduces the feature dimension and computational complexity, and obtains a higher pedestrian detection rate and robust detection performance.

Deep Learning for Pedestrian Detection

2019 ◽  
Vol 9 (8) ◽  
pp. p9212
Author(s):  
Utkarsha Sagar ◽  
Ravi Raja ◽  
Himanshu Shekhar
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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