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Airborne image sequences provide useful data for monitoring traffic scenes in large complex urban environments. Tracking vehicles in these low frame rate images is challenging, because of abrupt motion and low resolution description of the targets. In this work, we propose a new particle based method for long-term tracking of multiple vehicles in airborne images. Our tracking system uses a vehicle-specific...
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...
License plate recognition systems have been widely applied in community and building parking-lots management at present. They are also used by public security organs to trace stolen vehicles and monitor roads performance. In this paper, a new method by combining background subtraction and shadow detecting has been proposed. Motor vehicles can be monitored automatically according to image sequence...
This paper presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application utilizes image-processing and pattern recognition methods designed and modified to the needs and constrains of road traffic analysis. These methods combined together gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking,...
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...
The INTERSAFE-2 project aims to develop and demonstrate a Cooperative Intersection Safety System that is able to significantly reduce injury and fatal accidents at intersections. The cooperative sensor data fusion is based on state-of-the-art and advanced on-board sensors for object recognition and relative localisation, a standard navigation map and information from other road users, infrastructure...
Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework performs two major steps. First, HFG-based...
The recognition of potentially hazardous situations on road intersections is an indispensable skill of future driver assistance systems. In this context, this study focuses on the task of vehicle tracking in combination with a long-term motion prediction (1-2 s into the future) in a dynamic scenario. A motion-attributed stereo point cloud obtained using computationally efficient feature-based methods...
Queue length after the stop line is an important parameter in the model of Intersection signal control which is the base of urban traffic control. In this paper, the detection algorithms of queue length by the image information are proposed. At first, the background differential is used to extract the vehicle after the stop line, and then the three regional of the left, straight and right are identified,...
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...
We present here a prototype of an algorithm for vehicle speed estimation. Different from previous approaches, our algorithm requires no road markers and fewer manual calibrations. Based on specific projection rules, we find a relation between the in-camera coordinate and the real world coordinate. A non-linear regression is employed to estimate the model parameters. This model enables us to estimate...
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...
Effective background reconstruction is the key for real time traffic flow monitoring. High traffic density and complexity of background scene make reconstruction more difficult. Background estimation based on the median method is imprecise under a complex traffic flow condition. In this paper, a new background estimation method based on the similarity of background using parameters of gray mean and...
Traffic incident detection is one of the most important issues for intelligent transportation systems (ITS), especially in urban area which is full of signaled intersections. This paper presents the development of a novel traffic incident detection system based on image signal processing, feature extraction algorithms, and hidden Markov model (HMM) classifier. First, a traffic surveillance system...
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...
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