Vehicle Detection vehicle detection , Tightly Coupled LIDAR and Computer Vision Integration for

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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Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection

10.1002/9781119748342.ch9 ◽

2021 ◽

pp. 167-196

Author(s):

Gabrielle Bakker‐Reynolds ◽

Emre Erturk ◽

Istvan Lengyel ◽

Noor Alani

Keyword(s):

Computer Vision ◽

Vehicle Detection

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What is next in real-time computer vision? An overview of tightly-coupled non-homogeneous multiprocessor architectures

[Proceedings] 1988 IEEE Workshop on Languages for Automation@m_Symbiotic and Intelligent Robotics ◽

10.1109/lfa.1988.24974 ◽

2003 ◽

Cited By ~ 1

Author(s):

A. Dollas

Keyword(s):

Computer Vision ◽

Real Time ◽

Multiprocessor Architectures ◽

Tightly Coupled

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Automated Failure Detection in Computer Vision Systems

Journal of Computational Vision and Imaging Systems ◽

10.15353/vsnl.v2i1.84 ◽

2016 ◽

Vol 2 (1) ◽

Author(s):

H. Yan ◽

A. Achkar ◽

Akshaya Mishra ◽

K. Naik

Keyword(s):

Neural Network ◽

Computer Vision ◽

Vehicle Detection ◽

Failure Detection ◽

Convolution Neural Network ◽

Vision Systems ◽

Testing Data ◽

Computer Vision Systems ◽

Human Validation

Human validation of computer vision systems increase their operatingcosts and limits their scale. Automated failure detection canmitigate these constraints and is thus of great importance to thecomputer vision industry. Here, we apply a deep neural networkto detect computer vision failures on vehicle detection tasks. Theproposed model is a convolution neural network that estimates theoutput quality of a vehicle detector. We train the network to learnto estimate a pixel-level F1 score between the vehicle detector andhuman annotated data. The model generalizes well to testing data,providing a mechanism for identifying detection failures.

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