Moving Object Refining in Traffic Monitoring Applications

2007 ◽  
Author(s):  
Kunfeng Wang ◽  
Qingming Yao ◽  
Xin Qiao ◽  
Shuming Tang ◽  
Fei-Yue Wang
Keyword(s):  
Traffic Monitoring ◽  
Moving Object ◽  
Monitoring Applications

Image acquisition system for traffic monitoring applications

1995 ◽  
Author(s):  
Glen Auty ◽  
Peter I. Corke ◽  
Paul Dunn ◽  
Murray Jensen ◽  
Ian B. Macintyre ◽  
...  
Keyword(s):  
Image Acquisition ◽  
Traffic Monitoring ◽  
Acquisition System ◽  
Monitoring Applications ◽  
Image Acquisition System

scholarly journals Moving Object Tracking Algorithm for Complex Shape and Occlusion Handling

2019 ◽  
Vol 9 (1) ◽  
pp. 6299-6304
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Traffic Monitoring ◽  
Block Matching ◽  
Moving Object ◽  
Subtraction Method ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Moving Object Tracking

An object tracking increases loads of enthusiasm for dynamic research in applications such as video surveillance, vehicle navigation, highways, crowded public places, borders, forest and traffic monitoring areas. The system we develop aims to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection and tracking. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

scholarly journals Implementation of a Mobile Platform Based on Fiber Bragg Grating Sensors for Automotive Traffic Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1567 ◽  
Author(s):  
Kivilcim Yuksel ◽  
Damien Kinet ◽  
Karima Chah ◽  
Christophe Caucheteur
Keyword(s):  
Fiber Optic ◽  
Traffic Monitoring ◽  
Fiber Optic Sensors ◽  
Mobile Platform ◽  
Vehicle Speed ◽  
Distributed Monitoring ◽  
Fiber Bragg Grating Sensors ◽  
Weigh In Motion ◽  
Monitoring Applications ◽  
Fbg Sensors

Instrumentation techniques, implementation and installation methods are major concerns in today’s distributed and quasi-distributed monitoring applications using fiber optic sensors. Although many successful traffic monitoring experiments have been reported using Fiber Bragg Gratings (FBGs), there has been no standardized solution proposed so far to have FBG seamlessly implemented in roads. In this work, we investigate a mobile platform including FBG sensors that can be positioned on roads for the purpose of vehicle speed measurements. The experimental results prove the efficiency of the proposed platform, providing a perspective toward weigh-in-motion systems.

Moving Object Detection Based on the Fish

2014 ◽  
Vol 644-650 ◽  
pp. 1253-1256 ◽  
Author(s):  
Lian Li ◽  
Jun Yi Song ◽  
Zhi Yang Yan
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Industrial Process ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Moving Object ◽  
Moving Target ◽  
Target Fish ◽  
The Military

The detection and tracking of moving object is the important research of image analysis and understanding as well as in computer vision field, and have extensive application in the traffic monitoring, the military, industrial process control and medical research, but less application in the underwater monitoring of fish. In this paper, in order to be able to real-time detection of the fish in the digital video system moving target, proposed the fish moving target detection algorithm under a camera. With an improved background updating method of adaptive Gaussian mixture model, a method to detect the target fish based on Gaussian mixture model combined with edge detection operator.

scholarly journals Design and Develop an Autonomous UAV Airship for Indoor Surveillance and Monitoring Applications

2018 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Hairol Nizam Mohd Shah ◽  
Mohd Zamzuri Ab Rashid ◽  
Zalina Kamis ◽  
Mohd Shahrieel Mohd Aras ◽  
Nursabillilah Mohd Ali ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Traffic Monitoring ◽  
Edge Detector ◽  
Canny Operator ◽  
Three Phase ◽  
Monitoring Applications ◽  
Security Inspection

This project is about to develop an airship based on small size remotely controlled by human. The airship is one of Unmanned Airship Vehicle (UAV) which is can be apply in advertising, VIP security inspection, traffic monitoring and management and so on. The main purpose of this project is to design and develop an autonomous UAV airship for indoor surveillance and monitoring applications. The image will be captured from wireless camera where it mounted at a bottom of gondola. To determine the centroid points of the object are implemented in three phase edge detector, canny operator and threshold. The object will be display on Graphical User Interface (GUI) in 2D coordinated. In this project the systems able to detect only one object at one time.

Query-Based Video Synopsis for Intelligent Traffic Monitoring Applications

2020 ◽  
Vol 21 (8) ◽  
pp. 3457-3468 ◽  
Author(s):  
Sekh Arif Ahmed ◽  
Debi Prosad Dogra ◽  
Samarjit Kar ◽  
Renuka Patnaik ◽  
Seung-Cheol Lee ◽  
...  
Keyword(s):  
Traffic Monitoring ◽  
Video Synopsis ◽  
Monitoring Applications

Comparative Study of Statistical background Modeling and Subtraction

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Mourad Moussa ◽  
Maha Hmila ◽  
Ali Douik
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Human Motion ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Scenes

Background subtraction methods are widely exploited for moving object detection in videos in many computer vision applications, such as traffic monitoring, human motion capture and video surveillance. The two most distinguishing and challenging aspects of such approaches in this application field are how to build correctly and efficiently the background model and how to prevent the false detection between; (1) moving background pixels and moving objects, (2) shadows pixel and moving objects. In this paper we present a new method for image segmentation using background subtraction. We propose an effective scheme for modelling and updating a background adaptively in dynamic scenes focus on statistical learning. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Unlike the traditional color levels provided by RGB sensor aren’t the best choice, for this reason we propose a recursive algorithm that contributes to select very significant color space. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model.

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