Pedestrian Detection Technology Research Articles

Optimal path for automated pedestrian detection: image deblurring algorithm based on generative adversarial network

The pedestrian detection technology of automated driving is also facing some challenges. Aiming at the problem of specific target deblurring in the image, this research built a pedestrian detection deblurring model in view of Generative adversarial network and multi-scale convolution. First, it designs an image deblurring algorithm in view of Generative adversarial network. Then, on the basis of image deblurring, a pedestrian deblurring algorithm in view of multi-scale convolution is designed to focus on deblurring the pedestrians in the image. The outcomes showcase that the peak signal to noise ratio and structural similarity index of the image deblurring algorithm in view of the Generative adversarial network are the highest, which are 29.7 dB and 0.943 dB respectively, and the operation time is the shortest, which is 0.50 s. The pedestrian deblurring algorithm in view of multi-scale convolution has the highest peak signal-to-noise ratio (PSNR) and structural similarity indicators in the HIDE test set and GoPro dataset, with 29.4 dB and 0.925 dB, 40.45 dB and 0.992 dB, respectively. The resulting restored image is the clearest and possesses the best visual effect. The enlarged part of the face can reveal more detailed information, and it is the closest to a real clear image. The deblurring effect is not limited to the size of the pedestrians in the image. In summary, the model constructed in this study has good application effects in image deblurring and pedestrian detection, and has a certain promoting effect on the development of autonomous driving technology.

Pedestrian Detection Based on Feature Enhancement in Complex Scenes

Pedestrian detection has always been a difficult and hot spot in computer vision research. At the same time, pedestrian detection technology plays an important role in many applications, such as intelligent transportation and security monitoring. In complex scenes, pedestrian detection often faces some challenges, such as low detection accuracy and misdetection due to small target sizes and scale variations. To solve these problems, this paper proposes a pedestrian detection network PT-YOLO based on the YOLOv5. The pedestrian detection network PT-YOLO consists of the YOLOv5 network, the squeeze-and-excitation module (SE), the weighted bi-directional feature pyramid module (BiFPN), the coordinate convolution (coordconv) module and the wise intersection over union loss function (WIoU). The SE module in the backbone allows it to focus on the important features of pedestrians and improves accuracy. The weighted BiFPN module enhances the fusion of multi-scale pedestrian features and information transfer, which can improve fusion efficiency. The prediction head design uses the WIoU loss function to reduce the regression error. The coordconv module allows the network to better perceive the location information in the feature map. The experimental results show that the pedestrian detection network PT-YOLO is more accurate compared with other target detection methods in pedestrian detection and can effectively accomplish the task of pedestrian detection in complex scenes.

Review of pedestrian detection technology based on deep learning

Pedestrian detection is one of the main research problems in the field of computer vision. With the development of deep learning technology, pedestrian detection algorithms have made great progress and breakthroughs. Currently, pedestrian detection technology using deep learning has become mainstream. The first type of algorithm is called two-stage detection algorithms, which are based on Region of Interest (ROI) and single-stage detection algorithms adopt an end-to-end training method. So this paper conducts extensive research on the basis of classification and comparison, studies the characteristics and development of the above two detection algorithms, respectively, and finally puts forward suggestions for their future development, such as the fusion of image background and target state.

Research on HOG-SVM pedestrian detection method based on FPGA

At present, pedestrian detection technology has become a research hotspot in the field of image processing and computer vision, and has developed to open up a wide range of application prospects. This paper gives a comprehensive introduction to the algorithm and FPGA implementation of object detection algorithm including HOG and SVM. In the field of pedestrian detection, HOG-SVM pedestrian detection method based on FPGA has unique advantages compared with other various object detection methods. In the current research, this implementation method usually has a detection accuracy rate of more than 95% and a detection speed of more than 30 frames/s for the INRIA data sets, which perfectly meets the pedestrian detection requirement. FPGA with its parallel structure and a large amount of programmable logic sources greatly improve the HOG-SVM to perform real-time pedestrian detection. With the development of hardware acceleration and detection algorithms, more and more pedestrian detection methods with better performance will appear in the future.

SSD-Resnet50: Research on Pedestrian Detection Technology

In the era of artificial intelligence, with the rapid development of computer hardware, the defects of neural network algorithm have been further improved, the technology of artificial intelligence is progressing, the algorithm and technology of target detection have been further improved, and pedestrian detection technology is an important research direction of target detection technology. The main research content of this paper consists of three parts: the improvement of the original SSD algorithm, and the fundamental composition and characteristics of the neural network; Changing VGG16, the basis of the original SSD algorithm, to Resnet50 can improve the detection speed and accuracy. A feature fusion module is introduced to fuse the feature map with large receptive field and small receptive field, so as to improve the overall expression ability of the feature map to the image.

Little-YOLOv4: A Lightweight Pedestrian Detection Network Based on YOLOv4 and GhostNet

Nowadays, the areas such as intelligent-assisted driving, intelligent monitoring, pedestrian analysis, and attitude detection are developing rapidly. Inseparable from these fields is pedestrian detection technology. In order to deploy pedestrian detection algorithms on devices with limited hardware resources, a recognition algorithm that takes into account both performance and speed is required. Thus, a Little-YOLOv4 network structure is proposed, where the GhostNet is used to extract image features and the PANet is ameliorated by adding BiFPN path fusion which can integrate richer semantic features and preserve spatial information. The DO-DConv and DSC take place of standard convolution and the ReLU6 replaces the Leaky ReLU, which reduce the computational cost. The squeeze-and-excitation network is added to YOLOv4 head network, which could greatly reduce the interference information. The pedestrian detection results show that the mAP is 90.11% and the FPS is 79 by using Little-YOLOv4. The Little-YOLOv4 network structure could achieve a good compromise between algorithmic accuracy and speed.

DETECTION OF PEDESTRIAN AND VEHICLES FOR ROAD SAFETY

Traffic accidents make car safety receive most attention in recent years. With the progress of image processing technology, the automotive safety equipment helps to conduct image processing with the videos captured by the cameras, which provides pedestrian safety. In the image-based active driving safety equipment, the pedestrian detection technology is important. We used development tool for implementation of cars and pedestrian detection in a video segment. This application will be developed in Python using OpenCV. Object detection exists in many countries around the world after recent growing interest for autonomous vehicles in the last decade. This project aims on a vision based approach focusing on vehicles and pedestrians detection in real-time as a perception. Hence, we can manage and control the traffic easily.

Pedestrian Recognition System of Wild Scene Video Streaming Based on Convolutional Neural Network

Object detection aims at detecting the position of important objects and identifying them in still images or video frames. Pedestrians are the objects with most attention in images and videos. We study the detection and recognition technology for pedestrian objects of the wild scene video streams. Based on convolutional neural network, we design an online pedestrian recognition system. As non-rigid objects, people may exist in different poses in the image. Moreover, with the interference of complex background, lighting changes and occlusions, it will be very difficult to detect and recognize human objects. Following pedestrian detection in the images, we can conduct research on pedestrian tracking, pedestrian recognition and pedestrian behavior analysis. In addition, pedestrian detection technology also has great commercial value in video monitoring systems, intelligent traffic systems and other fields. In this paper, we propose a network model based on multiple loss functions, which combines the cross-entropy loss function with the improved monitoring signal center loss function to update the model parameters. Finally, the extracted features are more obvious, making the features of the same individual more compact. Compared to networks trained with only cross-entropy loss functions, our method significantly improves recognition accuracy and implements a pedestrian attribute recognition application based on Web technology. And compared with other similar applications, the recognition system we designed is cross-platform and lightweight. It mainly includes image acquisition, pedestrian attribute recognition and data storage modules, which is of great significance for the application of pedestrian recognition technology in the wild scenes.

Pedestrian Detection Algorithm Combining Attention Mechanism and Nonmaximum Suppression Method

With the involution of pedestrian detection technology, higher requirements are put forward for the detection accuracy under the conditions of insufficient light, target occlusion, and too small scale. Without information and multiscale pedestrian target, visible light single-mode pedestrian detection algorithm has poor performance. To solve the above problems, a pedestrian detection algorithm combining attention mechanism and nonmaximum suppression method is proposed in this study, aiming to improve the accuracy of pedestrian detection. In addition, residual network ResNet-50 and IoU (intersection over union) loss function are also adopted to improve pedestrian detection accuracy. Attention mechanism was used to optimize and highlight pedestrian area features, and meanwhile, the nonmaximum suppression method was applied to improve the robustness of the algorithm. Experimental results show that the detection accuracy of the proposed algorithm is significantly higher than that of the traditional convolutional neural network algorithm.

Pedestrian Detection with Multi-View Convolution Fusion Algorithm.

In recent years, the pedestrian detection technology of a single 2D image has been dramatically improved. When the scene becomes very crowded, the detection performance will deteriorate seriously and cannot meet the requirements of autonomous driving perception. With the introduction of the multi-view method, the task of pedestrian detection in crowded or fuzzy scenes has been significantly improved and has become a widely used method in autonomous driving. In this paper, we construct a double-branch feature fusion structure, the first branch adopts a lightweight structure, the second branch further extracts features and gets the feature map obtained from each layer. At the same time, the receptive field is enlarged by expanding convolution. To improve the speed of the model, the keypoint is used instead of the entire object for regression without an NMS post-processing operation. Meanwhile, the whole model can be learned from end to end. Even in the presence of many people, the method can still perform better on accuracy and speed. In the standard of Wildtrack and MultiviewX dataset, the accuracy and running speed both perform better than the state-of-the-art model, which has great practical significance in the autonomous driving field.

A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning.

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

Research on pedestrian detection technology based on the SVM classifier trained by HOG and LTP features

In pedestrian detection, it is often affected by factors such as changes in the position of moving targets, changes in illumination, and complex background interference. Therefore, the traditional pedestrian feature detection method is affected by the complex environment, which will cause a certain deviation in the detection accuracy. This study combines hog and LTP (Local Trinary Patterns) feature detection technology to improve the efficiency of pedestrian detection. Constructing a pedestrian detection system based on SVM (Support Vector Machine) classifier trained by hog and LTP features, and constructs a pedestrian detection system according to the actual needs. Moreover, this paper designs a pedestrian detection experiment based on HOG (Histogram of Oriented Gradient) and LTP feature training SVM classifier. In addition, this paper uses the weighted fusion method to fuse the color map features with the depth map features, and finally uses the classifier to detect pedestrians. Experiments show that the new pedestrian detection system has a certain effect.

Pedestrian Detection Method Based on Deep Convolution Neural Network

Compared with the traditional pedestrian detection technology, the pedestrian detection technology based on deep learning has achieved overwhelming advantages. However, due to the large scale of deep convolution network, the demand for dedicated processor limits the popularization of pedestrian detection system. To solve these problems, this paper proposes a deep convolution network model with moderate network scale, which improves the universality of the detection model on the premise of ensuring the detection accuracy. Based on the low dimensional shallow convolution neural network, the optimal network structure is found from three aspects of network layer number, sensor field size and characteristic graph, and the final network parameters are determined by guiding experimental evaluation. The pedestrian image to be detected is input into the above network model, and the pedestrian detection result is obtained. The pedestrian detection results on several popular pedestrian databases such as Daimler, MIT and INRIA show that the network structure designed in this paper not only has moderate network size, but also has good detection performance. Cross experiments also verify the robustness and generalization of the algorithm. The work of this article is funded by the undergraduate training program for innovation and entrepreneurship (202010373050, 202010373051, 202010373046).

Adaptive Fusion of Multi-Scale YOLO for Pedestrian Detection

Although pedestrian detection technology is constantly improving, pedestrian detection remains challenging because of the uncertainty and diversity of pedestrians in different scales and of occluded pedestrian modes. This study followed the common framework of single-shot object detection and proposed a divide-and-rule method to solve the aforementioned problems. The proposed model introduced a segmentation function that can split pedestrians who do not overlap in one image into two subimages. By using a network architecture, multiresolution adaptive fusion was performed on the output of all images and subimages to generate the final detection result. This study conducted an extensive evaluation of several challenging pedestrian detection data sets and finally proved the effectiveness of the proposed model. In particular, the proposed model achieved the most advanced performance on data sets from Visual Object Classes 2012 (VOC 2012), the French Institute for Research in Computer Science and Automation, and the Swiss Federal Institute of Technology in Zurich and obtained the most competitive results in a triple-width VOC 2012 experiment carefully designed by the present study.

A Method of Infrared Image Pedestrian Detection with Improved YOLOv3 Algorithm

The principle of infrared image is thermal imaging technology. Infrared pedestrian detection technology can be applied to the safety monitoring of the elderly, which can not only protect personal privacy, but also realize pedestrian identification at night, which has strong application value and social significance. A method of infrared image pedestrian detection with improved YOLOv3 algorithm is proposed to increase the detection accuracy and solve the problem of low detection accuracy caused by infrared pedestrian target edge blurring. And according to the characteristics of infrared pedestrian, a complex sample data set is established which is applied to infrared pedestrian detection. The infrared image enhancement method with WDSR-B is adopted to improve the clarity of the data set. In addition, based on YOLOv3 algorithm, the output of the 4-time down-sampling layer is added to obtain richer context information for small targets and improve the detection performance of the network for small-target pedestrians. And the improved YOLOv3 network is trained by the enhanced infrared data set. Experimental results show that the scheme precision of pedestrian detection is higher than that of YOLOv3 algorithm. Therefore, this method can be applied to the detection of pedestrians at night and the safety monitoring of the elderly.

The Practical Aapplication of the YOLOv3 Model from the Perspective of Machine Learning in Pedestrian Detection

Target detection and tracking technology is particularly important in the field of computer vision. With the needs of social production and the development of science and technology, the research of video pedestrian detection and tracking technology based on mobile platforms also needs to be put on the agenda. In this paper, the mobile robot is based on the Gmapping method to map the unknown environment, based on the A* and Dijistra algorithm to complete the safety inspection in the known environment, collect, record and save the video during the inspection process,and based on YOLOv3, the pedestrian target in the video for detection, the Deep_Sort target tracking algorithm establishes tracking of the dectected target, and creates a learning sample data set 2019.

Survey of pedestrian detection with occlusion

Pedestrian detection is widely applied in surveillance, autonomous robotic navigation, and automotive safety. However, there are many occlusion problems in real life. This paper summarizes the research progress of pedestrian detection technology with occlusion. First, according to different occlusion, it can be divided into two categories: inter-class occlusion and intra-class occlusion. Second, it summarizes the traditional method and deep learning method to deal with occlusion. Furthermore, the main ideas and core problems of each method model are analyzed and discussed. Finally, the paper gives an outlook on the problems to be solved in the future development of pedestrian detection technology with occlusion.

Research on Pedestrian Detection Algorithm Based on Deep Learning

Pedestrian detection is a kind of computer vision technology which is used to judge whether there is a pedestrian in a given image or video and estimate its position accurately. Pedestrian detection technology is widely used in intelligent security monitoring, driverless and robot fields. Pedestrian detection technology has nearly 30 years of development. Although it has greatly improved in accuracy and speed, there are problems such as lighting, pedestrian individual differences, occlusion and multi-scale pedestrian targets in the actual application scenarios, which lead to more missed and false detection in pedestrian detection. The algorithm is not robust enough, which makes the actual deployment difficult.

Detection of panoramic vision pedestrian based on deep learning

As the AI (artificial intelligence) develops, driverless vehicle technology is widely concerned, and the important problem that needs to be solved in driverless technology is the detection of pedestrians in the panoramic vision of the vehicle, so the pedestrian detection technology of panoramic vision is explored based on the deep learning. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, some adaptations based on a detector, CSP (center and scale prediction) are proposed. The original CSP of pedestrian detector is improved to make the training process more robust. For example, we use SN layers to replace all BN layers and ResNet-101 is used as backbone based on the research of deep learning. The main contributions of our paper are: (1) we improve the robustness of CSP and make it easier to train. (2) we propose a novel method to predict width, namely compressing width. (3) we achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate (MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy. (4) we explore some capabilities of switchable normalization which are not mentioned in its original paper. This study will provide important theoretical support and practical basis for pedestrian detection

Design and Implementation of Pedestrian Flow Monitoring and Warning Platform in Public Area based on Pedestrian Detection

In the traditional public area pedestrian flow monitoring, a lot of manpower and material resources are needed. Only relying on people’s eyes to identify and count the number of people in the video under monitoring is not only inefficient, but also has errors in accuracy and wastes a certain amount of energy and financial resources. In the rapid development trend of artificial intelligence, this system starts from the perspective of real-time monitoring, with the maturity and development of machine vision technology, and creates a cloud monitoring platform based on JavaWeb technology. The pedestrian detection technology is applied to the pedestrian flow monitoring, which provides a feasible scheme for accurate and fast pedestrian flow monitoring and warning, and develops a system to help public area managers and government departments to deal with the crowd gathering situation, analyze and monitor the crowd density, and give a quick warning to the crowd outbreak and gathering place.