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In this paper, we address the problem of vehicle detection and tracking with low-angle cameras by combining windshield detection and feature points clustering, effectively fusing several primitive image features such as color, edge and interest point. By exploring various heterogenous features and multiple vehicle models, we achieve at least two improvements over the existing methods: higher detection...
Overspeed is a major factor causing road traffic accidents in China. Since the early 1980s, researchers have developed various algorithms to extract speed information from traffic image sequences. In this paper, an algorithm is proposed to evaluate the speed of the accident vehicle by videos. With human interaction, control points are refined by corner detector and then used for two-dimension geometric...
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road...
This paper proposes a novel vehicle detecting approach for surveillance scenes with single stationary camera. Difference accumulative based background modeling method is used for background modeling. Background subtraction operation is used for detecting moving vehicles and Otsu method is used to threshold the background difference image. Subtractive clustering algorithm is applied for vehicle locating...
This paper proposes a new road traffic monitoring method based on image processing and particle filtering. The proposed method detects and classifies automatically moving vehicles in previously defined classes. The detected vehicles are tracked using a new particle filtering algorithm to determine their positions on the road at each time, and then the vehicle positions are used to estimate its trajectory...
In this paper, we formulate the feature clustering problem for vehicle detection and tracking as a general MAP problem and solve it using MCMC. The proposed approach exhibits two advantages over existing methods: general Bayesian model can handle arbitrary objective functions and MCMC guarantees global optimal solution. Our algorithm is validated on real-world traffic video sequences, and is shown...
Due to the recent progress in computer vision to interpret images and sequence of images, the video camera is a promising sensor for traffic monitoring and traffic surveillance at low cost. This paper focuses on the detection and tracking of multiple vehicles present in the field of view of a camera. Until now, the vehicle detection has been mainly performed by the widely used technique called background...
For an intelligent transportation system (ITS), traffic incident detection is one of the most important issues, especially for urban area which is full of signaled intersections. In this paper, we propose a novel traffic incident detection method based on the image signal processing and hidden Markov model (HMM) classifier. First, a traffic surveillance system was set up at a typical intersection...
In modern intelligent transportation systems, the video image vehicle detection system (VIVDS) is gradually becoming one of the popular methods at signalized traffic intersection due to its convenient installation and rich information content provided. However, in the current VIVDS, the camera usually is installed at the roadside poles or traffic light poles, which not only requires more than one...
This paper presents a practical real-time traffic monitoring system based on object detection and tracking for measuring traffic parameters such as speed and volume. The system presented in this paper is a following work from our previous works in. This monitoring system is currently being used on real traffic environment in Thailand. The results in this paper confirm the validity of the proposed...
This paper presents methods for vision-based detection and tracking of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. The goal of this research is to develop suitable methods for automatic visual traffic surveillance to perform detection, tracking and traffic parameter estimation of multiple vehicles in real time as well as tackle environment illumination...
This paper is dedicated to detecting and counting vehicles in day environment by using real time traffic flux through differential techniques. The basic idea used is variation in the traffic flux density due to presence of vehicle in the scene. In the present work a simple differential algorithm is designed and tested with vehicle detection and counting application. Traffic flux estimation will play...
Computational structure of cellular automata has attracted researchers and vastly been used in various fields of science. They are especially suitable for modeling natural systems that can be described as massive collections of simple objects interacting locally with each other, such as motion detection in image processing. On the other hand, extraction of moving objects from an image sequence is...
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