The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Event detection is an important research in video surveillance technology. This paper proposed a method for traffic event detection based on visual Mechanism on the background of traffic video surveillance applications. In this method, based on the extraction of video target motion characteristics, it extracted abnormal targets mainly through the features merging and significant competitive in video...
This paper proposes a scene segmentation method for foreground detection in outdoor visual surveillance. An outdoor scene is divided into three parts: road, sky, and other region by using seeded region growing (SRG) algorithm in which road region gets initial seeds by using the motion information of vehicles and sky region gets initial seeds by using a probabilistic classifier. As a result, instead...
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 work aims at detecting and tracking vehicles in in-car video. Rather than enhancing shape analysis of various vehicle types and road situations, this work focuses on vehicle and background motions because they are more general than shapes and colors of cars in various road environments. Basic features are tracked stably using corners, intensity peaks, and horizontal line segments. We use the...
We present and evaluate a novel scene descriptor for classifying urban traffic by object motion. Atomic 3D flow vectors are extracted and compensated for the vehicle's egomotion, using stereo video sequences. Votes cast by each flow vector are accumulated in a bird's eye view histogram grid. Since we are directly using low-level object flow, no prior object detection or tracking is needed. We demonstrate...
This paper presents a novel approach for road course estimation on rural roads using a mulitlayer laser scanner. The measurements of the sensor are used to build an occupancy grid as a representation of a local map. This mapping step uses a new free space function and a novel method for detecting and eliminating moving objects. Based on this map a feature extraction algorithm yields road border feature...
This paper presents a real-time moving object detection method using the wavelet-based neural networks for the pre-crash safety system of a vehicle. The proposed method uses stationary infrared cameras to sense vehicles and obstacles on the road ahead, and the CarPC to determine whether or not a collision is based on the speed, position, and traveling route of the object. In this method, the stationary...
A novel vision-based road detection method was proposed in this paper to realize visual guiding navigation for ground mobile vehicles in outdoor environments. The road region was first segmented from the jumbled backgrounds by using an adaptive threshold segmentation algorithm named OTSU. Subsequently, the Canny edges extracted in grey images would be filtered in the road region so that the road boundary...
Moving vehicle recognition and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a binary discrete wavelet transforms based moving object recognition algorithm is put forward, which directly detects moving vehicles in the binary discrete wavelet transforms domain. For the shortages of RGB or HSV color...
This paper presents detection and classification of moving Thai vehicles based on traffic engineering knowledge. The proposed technique consists of two main parts as follows. The first part is the detection of moving vehicles using image tracking methods e.g. background and foreground (BG/FG) detection and blob tracking. Such methods can provide the values of vehicle features such as position, length...
With the increase of vehicles, the work load and the difficulties of the traffic management and road tolling become harder and harder, and day by day, so the automatic recognition of automobile type has very important real value in the traffic management system and road auto-tolling system. An approach is presented to detect and classify the moving vehicle in static scenes, which is based on GVF-Snake...
Here we propose a complete system for robust detection and recognition of the current speed sign restrictions from a moving road vehicle. This approach includes the detection and recognition of both numerical limit and national limit (cancellation) signs with the addition of automatic vehicle turn detection. The system utilizes both RANSAC-based colour-shape detection of speed limit signs and neural...
This paper describes the idea and the implementation of the robust algorithm dedicated to extraction of moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed. The authors, apart from the careful selection of particular steps of the algorithm towards hardware implementation, also...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.