Intelligent Vehicle Vision Technology: Tightly Coupled LIDAR and Computer Vision Integration for Vehicle Detection

With the rapid growth of the global economy, the global car ownership is also increasing year by year, which has caused a series of problems, the most prominent of which is traffic congestion and traffic accidents. In order to solve the traffic problem, all countries are actively studying the intelligent transportation system, and one of the important research contents of the intelligent transportation system is vehicle detection. Vehicle detection based on vision is to capture vehicle images in the driving environment through a camera, and then use computer vision recognition technology for vehicle detection and recognition. Although computer vision recognition technology has made great progress, how to improve the detection accuracy of the image to be detected is still an important content of visual recognition technology research. Intelligent vehicle visual robust detection and identification of methods of research to reduce the growing incidence of traffic accidents, improve the existing road traffic safety and transportation efficiency, alleviate the degree of driver fatigue problem are of great significance. This paper considers the intelligent vehicle environmental awareness of the key technology to the goal of robust detection and recognition based on machine vision problems for further research. The particle filter is used to extract the local energy of the image to realize the fast segmentation of the region of interest (ROI). In order to further verify the ROI, a measure learning method based on multi-core embedding is proposed, and the semantic classification of ROI is realized by integrating the color, shape and geometric features of ROI. Experimental results show that the algorithm can effectively eliminate false sexy ROI interest, and the algorithm is robust to complex background, illumination changes, perspective changes and other conditions.

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An AHP-Based Evaluating Model to Evaluate Intelligent Vehicle Detection Technologies in Street Parking Scenario

CICTP 2018 ◽

10.1061/9780784481523.141 ◽

2018 ◽

Author(s):

Xuejin Wan ◽

Shangfo Huang ◽

Bowen Du ◽

Rui Sun ◽

Jiong Wang ◽

...

Keyword(s):

Vehicle Detection ◽

Intelligent Vehicle ◽

Detection Technologies

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Vehicle Detection through Instance Segmentation using Mask R-CNN for Intelligent Vehicle System

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) ◽

10.1109/iciccs51141.2021.9432374 ◽

2021 ◽

Author(s):

Apoorva Ojha ◽

Satya Prakash Sahu ◽

Deepak Kumar Dewangan

Keyword(s):

Vehicle Detection ◽

Intelligent Vehicle ◽

Vehicle System ◽

Instance Segmentation

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Road Traffic Analysis Using Computer Vision

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37630 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2002-2006

Author(s):

Needhi U. Gaonkar

Keyword(s):

Computer Vision ◽

Traffic Flow ◽

Traffic Management ◽

Road Traffic ◽

Vehicle Detection ◽

Traffic Analysis ◽

Transportation Systems ◽

Visual Object ◽

Highway Traffic ◽

Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

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