scholarly journals Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Gang Li ◽  
Huansheng Song ◽  
Shuyu Wang ◽  
Jinliang Kong
Keyword(s):  
Background Subtraction ◽  
Three Dimensional ◽  
Vehicle Detection ◽  
Reconstruction Algorithm ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
License Plate ◽  
Surveillance Systems ◽  
Subtraction Method ◽  
2D Images

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the three-dimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods.

scholarly journals Improving Vehicle Detection Accuracy Based on Vehicle Shadow and Superposition Elimination

2015 ◽  
Vol 9 (1) ◽  
pp. 1039-1044 ◽  
Author(s):  
Hongjin Zhu ◽  
Honghui Fan ◽  
Feiyue Ye ◽  
Xiaorong Zhao
Keyword(s):  
Color Space ◽  
Great Influence ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
Surveillance Systems ◽  
Traffic Surveillance ◽  
Hsv Color Space ◽  
Shadow It ◽  
Background Models

Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper presented a method which improves Gaussian mixture model to get adaptive background. The HSV color space was used to eliminate vehicle shadow, it was based on a computational colour space that makes use of our shadow model. Vehicle superposition elimination was discussed based on vehicle contour dilation and erosion method. Experiments were performed to verify that the proposed technique is effective for vehicle detection based traffic surveillance systems. Detection results showed that our approach was robust to widely different background and illuminations.

scholarly journals Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 508
Author(s):  
Feng-Shuo Hsu ◽  
Tang-Chen Chang ◽  
Zi-Jun Su ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen
Keyword(s):  
Density Functional ◽  
Probability Model ◽  
Three Dimensional ◽  
Fall Detection ◽  
The Elderly ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
Movement Trajectory ◽  
Motion Data ◽  
Comparable Performance

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.

A Novel Background Subtraction Method for Moving Vehicle Detection

2012 ◽  
Vol 132 (10) ◽  
pp. 857-863 ◽  
Author(s):  
Xiaofeng Lu ◽  
Takashi Izumi ◽  
Lin Teng ◽  
Tadahiro Horie ◽  
Lei Wang
Keyword(s):  
Background Subtraction ◽  
Vehicle Detection ◽  
Subtraction Method ◽  
Moving Vehicle ◽  
Moving Vehicle Detection

Fusion of Background Subtraction and Frame Difference in HSV Space for Multi-Object Detection

2013 ◽  
Vol 380-384 ◽  
pp. 3895-3899
Author(s):  
Ya Ne Wen ◽  
Hong Song Li ◽  
Hao Zhou ◽  
Li Ping Tang ◽  
Jun Qi She
Keyword(s):  
Difference Method ◽  
Background Subtraction ◽  
Gaussian Mixture ◽  
Test Results ◽  
Subtraction Method ◽  
False Detection ◽  
Strong Light ◽  
Fusion Frame ◽  
Frame Difference ◽  
Light And Shadow

in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.

An Optimization Algorithm of Foreground Objects Extraction

2011 ◽  
Vol 403-408 ◽  
pp. 169-176
Author(s):  
Xia Yi Zhang ◽  
Zhi Peng Li ◽  
Fu Qiang Liu ◽  
Zhen Jia ◽  
Jian Wei Zhao
Keyword(s):  
Background Subtraction ◽  
Gaussian Mixture Models ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Object Extraction ◽  
Detection Accuracy ◽  
Foreground Object ◽  
Image Matting ◽  
New Approach ◽  
Novel Approach

In this paper, we propose a novel algorithm for coarse-to-fine foreground objects extraction. There are two general approaches for foreground objects extraction: background subtraction and image matting. Our new approach can not only improve detection accuracy compared with general background subtraction approaches, but also reduce computation burden compared with general image matting approaches. Firstly, we present a novel method called Motion-mask Gaussian Mixture Models (Motion-mask GMMs) to extract coarse foreground regions. This new approach can classify foreground and background pixels more accurately, especially when there are long-time stopping objects in the scene. Secondly, with the coarse foreground regions, we propose a novel approach to make foreground object extraction more accurate based on effective fusion of image registration and image matting. This new method overcomes the template drift problem during template updating and also reduces the expensive computational cost of image matting. Our proposed approach is tested with kinds of video sequences in indoor and outdoor environments. Experimental results demonstrate the accuracy and efficiency of our proposed approach for foreground object extraction.

scholarly journals License plate recognition for campus auto-gate system

2021 ◽  
Vol 21 (1) ◽  
pp. 128
Author(s):  
Nur Liyana Yaacob ◽  
Ammar Ahmed Alkahtani ◽  
Fuad M. Noman ◽  
Ahmad Wafi Mahmood Zuhdi ◽  
Dhuha Habeeb
Keyword(s):  
Automated System ◽  
Detection Accuracy ◽  
Portable Devices ◽  
License Plate ◽  
Surveillance Systems ◽  
University Campus ◽  
Local Conditions ◽  
Gate System ◽  
Licence Plate Recognition ◽  
The University

<p><span>Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition.</span></p>

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Sign in / Sign up

Export Citation Format

Share Document