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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...
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...
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...
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...
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...
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...
The traffic safety monitoring technology becomes an active research field due to the development of the city's transport system. In this paper, we design a warning method in traffic video monitoring through analyzing vehicle states. This method is based on image analysis and processing technology, extracting the information of the characteristics of the movement of vehicles through detecting and tracking...
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