Real Time Pedestrian Detection Algorithm by Mean Shift

2014 ◽  
Vol 9 (6) ◽  
Author(s):  
Qing Tian ◽  
Shuai Qiao ◽  
Teng Guo ◽  
Yun Wei
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Mean Shift ◽  
Detection Algorithm

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals Real-time Pedestrian Detection Algorithm Based on Improved YOLOv3

2021 ◽  
Vol 2002 (1) ◽  
pp. 012075
Author(s):  
Xianchang Xi ◽  
Zhikai Huang ◽  
Lingyi Ning ◽  
Yang Zhang
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Detection Algorithm

A REAL-TIME DETECTION ALGORITHM FOR VISION-BASED PEDESTRIAN PROTECTION

2008 ◽  
Vol 05 (01) ◽  
pp. 11-30 ◽  
Author(s):  
GUANGLIN MA ◽  
SU-BIRM PARK ◽  
ALEXANDER IOFFE ◽  
STEFAN MÜLLER-SCHNEIDERS ◽  
ANTON KUMMERT
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Ground Plane ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Low Contrast ◽  
The Road ◽  
Novel Approach ◽  
Real Time Detection ◽  
Flat Ground

This paper discusses the robust, real-time detection of stationary and moving pedestrians utilizing a single car-mounted monochrome camera. First, the system detects potential pedestrians above the ground plane by combining conventional Inverse Perspective Mapping (IPM)-based obstacle detection with the vertical 1D profile evaluation of the IPM detection result. Usage of the vertical profile increases the robustness of detection in low-contrast images as well as the detection of distant pedestrians significantly. A fast digital image stabilization algorithm is used to compensate for erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low-level pedestrian-oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented. A novel approach termed Pedestrian Detection Strip (PDS) is used to improve the calculation time by a factor of six compared to conventional approaches.

Research and Implementation of Real-Time Pedestrian Detection Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 1837-1841
Author(s):  
Mei Hua Xu ◽  
Huai Meng Zheng ◽  
Chen Jun Xia
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Local Binary Patterns ◽  
Detection Algorithm ◽  
Typical Feature ◽  
Detection Methods ◽  
Driving System ◽  
The Real ◽  
Time Performance ◽  
Speed Up

Pedestrian detection has a broad application prospect in automotive assisting driving system, but the real time performance is very poor in most common used detection methods. This paper presents a fast algorithm to realize the real-time pedestrian detection. The Local Binary Patterns (LBP) is used to describe the local texture information with the feature of less calculation, the HOG classifier to extract a typical feature of pedestrian’s edge, and then SVM to train and classify on the databases of INRIA and MIT. While scanning the images, interest regions are extracted to speed up the detection. Series of experiment results shows that the proposed pedestrian detecting strategy is effective and efficient.

scholarly journals Real-Time Detection of Linear Structure Objects Using Mean Shift Segmentation and Heuristic Search

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiying Cai ◽  
Lida Zou ◽  
Peng Lv ◽  
Lingqiang Ran
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Shape Matching ◽  
Mean Shift ◽  
Linear Structure ◽  
Detection Algorithm ◽  
Search Method ◽  
Detection Accuracy ◽  
Heuristic Search Method ◽  
Detection Of Objects

With the development of intelligent industrial production, industrial components with linear structure tend to be regular, such as TV LCD module, mobile phone screen, and electronic equipment shell. Recognition of linear structure objects by machine vision is an important aspect of intelligent industry. At present, shape matching algorithm is mostly used for arbitrary structure objects. It will be time-consuming if it is directly used to detect the linear structure objects as it needs to traverse the parameter space of the object. To solve the traversal problem and detect the linear structure objects in real time, a heuristic detection algorithm is designed according to the characteristics of linear structure objects. First, the coarse position and orientation are obtained by mean shift filtering and heuristic region grouping to reduce the searching range. Then, the heuristic search method is used to get the precise location information. The heuristic search method is designed based on the particle swarm optimization algorithm and heuristic information. The proposed method has been evaluated on two image databases of common industrial parts and backlight units which are typical linear structure objects. The experimental results showed that the proposed algorithm could reduce the detect time by more than 70% averagely while the detection accuracy is kept. It proves that the proposed algorithm can detect linear structure objects in real time and is suitable for the detection of objects with linear structures.

scholarly journals A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm

2022 ◽  
Vol 355 ◽  
pp. 03020
Author(s):  
Yitong Mao
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Low Light ◽  
Convolutional Network ◽  
Detection Model ◽  
Aerial View ◽  
Weak Light ◽  
Ap Index ◽  
Dense Crowd

The real-time pedestrian detection algorithm requires the model to be lightweight and robust. At the same time, the pedestrian object detection problem has the characteristics of aerial view Angle shooting, object overlap and weak light, etc. In order to design a more robust real-time detection model in weak light and crowded scene, this paper based on YOLO, raised a more efficient convolutional network. The experimental results show that, compared with YOLOX Network, the improved YOLO Network has a better detection effect in the lack of light scene and dense crowd scene, has a 5.0% advantage over YOLOX-s for pedestrians AP index, and has a 44.2% advantage over YOLOX-s for fps index.

scholarly journals Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3646
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Silun Peng ◽  
Shixin Song ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Clustering Algorithm ◽  
Pedestrian Detection ◽  
Grid Cell ◽  
Detection Algorithm ◽  
Intelligent Vehicles ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Driving Assistance

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

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