Design and Evaluation of a Real-time Pedestrian Detection System for Autonomous Vehicles

2020 ◽  
Author(s):  
KB Pranav ◽  
J Manikandan
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Pedestrian Detection

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 A PRELIMINARY STUDY ON UPDATING HIGH DEFINITION MAPS: DETECTING AND POSITIONING A TRAFFIC CONE BY USING A STEREO CAMERA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 271-274
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Time Data ◽  
High Definition ◽  
Stereo Camera ◽  
The Road ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous Vehicles (AV) or self-driving cars has been increasingly popular these past few years. As such, research and development of AVs have also escalated around the world. One of those researches is about High-Definition (HD) maps. HD Maps are basically very detailed maps that provide all the geometric and semantic information on the road, which helps the AV in positioning itself on the lanes as well as mapping objects and markings on the road. This research will focus on the early stages of updating said HD maps. The methodology mainly consists of (1) running YOLOv3, a real-time object detection system, on a photo taken from a stereo camera to detect the object of interest, in this case a traffic cone, (2) applying the theories of stereo-photogrammetry to determine the 3D coordinates of the traffic cone, and (3) executing all of it at the same time on a Python-based platform. Results have shown centimeter-level accuracy in terms of obtained distance and height of the detected traffic cone from the camera setup. In future works, observed coordinates can be uploaded to a database and then connected to an application for real-time data storage/management and interactive visualization.

scholarly journals Vision-Based Deep Learning Algorithm for Detecting Potholes

2021 ◽  
Author(s):  
Kanushka Gajjar ◽  
Theo van Niekerk ◽  
Thomas Wilm ◽  
Paolo Mercorelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Detection System ◽  
Detection Methods ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Pothole Detection ◽  
Adequate Data

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

scholarly journals Design and Implementation of Vehicle Pedestrian Detection System Based on FPGA

2011 ◽  
Vol 2-3 ◽  
pp. 495-500
Author(s):  
Xue Wen Ma ◽  
Shuang Ma ◽  
Meng Yao Li ◽  
Mei Ling Jin
Keyword(s):  
Real Time ◽  
Detection System ◽  
Pedestrian Detection ◽  
Practical Significance ◽  
Clock Frequency ◽  
Driver Assistance ◽  
Broad Application ◽  
Design And Implementation ◽  
Pedestrian Accidents ◽  
Application Prospects

The vehicle pedestrian detection system is a kind of solution to car driver assistance. The system can detect pedestrian in dangerous and send early warning to drivers automatically. Therefore, it has practical significance to develop a robust、real-time and stability pedestrian detection system which can reduce and avoid pedestrian accidents effectively. This article completes the vehicle pedestrian detection system based on FPGA. It used the cyclone II EP2C70 DSP development board provided by the Altera Corporation. By testing, the system can determine a size of 320 × 240 grayscale image takes about 723ms at the clock frequency in the 100MHZ. It achieves the desired functionality. The system has better real-time and reliability. At the same time, it has small size, easy to control and the most important is that it has broad application prospects.

scholarly journals Design and Implementation of an Assistive Real-Time Red Lionfish Detection System for AUV/ROVs

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
M-Mahdi Naddaf-Sh ◽  
Harley Myler ◽  
Hassan Zargarzadeh
Keyword(s):  
Real Time ◽  
Coastal Waters ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Low Cost ◽  
Interface Energy ◽  
Depth Range ◽  
Air Supply ◽  
Natural Predator ◽  
Red Lionfish

In recent years, the Pterois Volitans, also known as the red lionfish, has become a serious threat by rapidly invading US coastal waters. Being a fierce predator, having no natural predator, being adaptive to different habitats, and being with high reproduction rates, the red lionfish has enervated current endeavors to control their population. This paper focuses on the first steps to reinforce these efforts by employing autonomous vehicles. To that end, an assistive underwater robotic scheme is designed to aid spear-hunting divers to locate and more efficiently hunt the lionfish. A small-sized, open source ROV with an integrated camera is programmed using Deep Learning methods to detect red lionfish in real time. Dives are restricted to a certain depth range, time, and air supply. The ROV program is designed to allow the divers to locate the red lionfish before each dive, so that they can plan their hunt to maximize their catch. Lightweight, portability, user-friendly interface, energy efficiency, and low cost of maintenance are some advantages of the proposed scheme. The developed system’s performance is examined in areas currently invaded by the red lionfish in the Gulf of Mexico. The ROV has shown success in detecting the red lionfish with high confidence in real time.

scholarly journals A REAL-TIME PEDESTRIAN DETECTION SYSTEM IN STREET SCENE

2016 ◽  
Vol 9 (3) ◽  
pp. 1592-1613 ◽  
Author(s):  
Ai-ying Guo ◽  
Mei-hua Xu ◽  
Feng Ran ◽  
Qi Wang
Keyword(s):  
Real Time ◽  
Detection System ◽  
Pedestrian Detection ◽  
Street Scene
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