SVM based Pedestrian Detection System for Sidewalk Snow Removing Machines

2021 ◽  
Author(s):  
Yuta Sasaki ◽  
Takanori Emaru ◽  
Ankit A. Ravankar
Keyword(s):  
Detection System ◽  
Pedestrian Detection

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

A new thermal infrared and visible spectrum images-based pedestrian detection system

2018 ◽  
Vol 78 (12) ◽  
pp. 15861-15885 ◽  
Author(s):  
Redouan Lahmyed ◽  
Mohamed El Ansari ◽  
Ayoub Ellahyani
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Visible Spectrum ◽  
Thermal Infrared

scholarly journals Integer arithmetic approximation of the hog algorithm used for pedestrian detection

2017 ◽  
Vol 14 (2) ◽  
pp. 329-346 ◽  
Author(s):  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Dubravko Culibrk ◽  
Darko Stefanovic ◽  
Bojan Lalic
Keyword(s):  
Classification Accuracy ◽  
System Performance ◽  
Detection System ◽  
Pedestrian Detection ◽  
Lessons Learned ◽  
People Detection ◽  
Algorithm Performance ◽  
Integer Arithmetic ◽  
Point Arithmetic ◽  
Angle Calculation

This paper presents the results of a study of the effects of integer (fixed-point) arithmetic implementation on classification accuracy of a popular open-source people detection system based on Histogram of Oriented Gradients. It is investigated how the system performance deviates from the reference algorithm performance as integer arithmetic is introduced with different bit-width in several critical parts of the system. In performed experiments, the effects of different bit-width integer arithmetic implementation for four key operations were separately considered: HoG descriptor magnitude calculation, HoG descriptor angle calculation, normalization and SVM classification. It is found that a 13-bit representation of variables is more than sufficient to accurately implement this system in integer arithmetic. The experiments in the paper are conducted for pedestrian detection and the methodology and the lessons learned from this study allow generalization of conclusions to a broader class of applications.

scholarly journals Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1174 ◽  
Author(s):  
Jian Luo ◽  
Chang Lin
Keyword(s):  
Real Time ◽  
Detection System ◽  
Pedestrian Detection ◽  
Image Sensor ◽  
Frame Rate ◽  
External Memory ◽  
Support Vector ◽  
Svm Classification ◽  
Prior Art ◽  
Memory Modules

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

scholarly journals Low-Power Pedestrian Detection System on FPGA

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 35 ◽  
Author(s):  
Vinh Ngo ◽  
David Castells-Rufas ◽  
Arnau Casadevall ◽  
Marc Codina ◽  
Jordi Carrabina
Keyword(s):  
Low Power ◽  
Detection System ◽  
Low Cost ◽  
Pedestrian Detection ◽  
Support Vector ◽  
Embedded Processor ◽  
Car Industry ◽  
Field Programmable ◽  
On Chip ◽  
Histogram Of Gradients

Pedestrian detection is one of the key problems in the emerging self-driving car industry. In addition, the Histogram of Gradients (HOG) algorithm proved to provide good accuracy for pedestrian detection. Many research works focused on accelerating HOG algorithm on FPGA (Field-Programmable Gate Array) due to its low-power and high-throughput characteristics. In this paper, we present an energy-efficient HOG-based implementation for pedestrian detection system on a low-cost FPGA system-on-chip platform. The hardware accelerator implements the HOG computation and the Support Vector Machine classifier, the rest of the algorithm is mapped to software in the embedded processor. The hardware runs at 50 Mhz (lower frequency than previous works), thus achieving the best pixels processed per clock and the lower power design.

PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS

2009 ◽  
Vol 23 (04) ◽  
pp. 801-823 ◽  
Author(s):  
CHI-CHEN RAXLE WANG ◽  
JIN-YI WU ◽  
JENN-JIER JAMES LIEN
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Pedestrian Detection ◽  
Linear Interpolation ◽  
Detection Performance ◽  
Experimental Results ◽  
Computational Time ◽  
Aspect Ratios ◽  
Histograms Of Oriented Gradients ◽  
Interpolation Technique

This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such that all the blocks extracted from the input images are rotationally invariant. The pixels within the cells in each block are then voted into rectangular- and circular-type 9-bin histograms of oriented gradients (HOGs) in accordance with their gradient magnitudes and corresponding multivariate Gaussian-weighted windows. Finally, four cell-based histograms are concatenated using a tri-linear interpolation technique to form one 36-dimensional normalized HOG feature vector for each block. The experimental results show that the use of the Gaussian-weighted window approach and tri-linear interpolation technique in constructing the HOG feature vectors improves the detection performance from 91% to 94.5%. In the proposed scheme, the detection process is performed using a cascaded detector structure in which the weak classifiers and corresponding weights of each stage are established using the AdaBoost self-learning algorithm. The experimental results reveal that the cascaded structure not only provides a better detection performance than many of the schemes presented in the literature, but also achieves a significant reduction in the computational time required to classify each input image.

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