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Proceedings ArticleDOI

AI Camera for Tracking Road Accidents

TL;DR: In this paper , it has been established after careful examination that the majority of accidents and in fatalities as a result of inadequate communication with the appropriate medical authorities and the following lack of urgent medical care.
Abstract: The average number of vehicles on the road worldwide has increased as cars and other vehicles become more and more accessible. Our lives are now easier because of the technology and infrastructure that are developing quickly. The advent of technology has also enhanced the risks associated with traffic and Regular traffic accidents result in a number of accidents on this dynamic planet and significant loss of life and property due to inadequate emergency facilities. Accident victims suffer greatly and lose significant time and money as a result. It has been established after careful examination that the majority of accidents and in fatalities as a result of inadequate communication with the appropriate medical authorities and the following lack of urgent medical care. There are many deaths as a result of inadequate crisis management. As a result, this research study intends to provide crisis administrations to the person who meets with an accident as soon as time permits
References
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Journal ArticleDOI
07 Nov 2002
TL;DR: This paper constructs a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations.
Abstract: Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Higher level understanding of events requires that certain lower level computer vision tasks be performed. These may include detection of unusual motion, tracking targets, labeling body parts, and understanding the interactions between people. To achieve many of these tasks, it is necessary to build representations of the appearance of objects in the scene. This paper focuses on two issues related to this problem. First, we construct a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations. Second, we build statistical representations of the foreground regions (moving objects) that support their tracking and support occlusion reasoning. The probability density functions (pdfs) associated with the background and foreground are likely to vary from image to image and will not in general have a known parametric form. We accordingly utilize general nonparametric kernel density estimation techniques for building these statistical representations of the background and the foreground. These techniques estimate the pdf directly from the data without any assumptions about the underlying distributions. Example results from applications are presented.

1,539 citations

Proceedings ArticleDOI
22 Oct 2007
TL;DR: This work suggests that an intersection collision detection system should be able to adapt to different types of intersections by acquiring the collision patterns of the intersection through data mining.
Abstract: The crash rate in road intersection demonstrates the need for a fast and accurate collision detection system. Ubiquitous computing research provides a significant opportunity to develop novel ways of improving road intersection safety. The existing intersection collision warning or avoidance systems are mostly built to suit a particular intersection. We suggest that an intersection collision detection system should be able to adapt to different types of intersections by acquiring the collision patterns of the intersection through data mining. Collision patterns that are specific to that intersection are stored in a knowledge base to select vehicles which are exposed to a high risk of collision. This algorithm increases the speed of collision detection calculation, as detection is not applied on all possible pairs in an intersection. The performance and accuracy of the algorithm are evaluated. This evaluation is done on a developed simulation bed and the results are presented.

56 citations

Book ChapterDOI
05 Dec 2005
TL;DR: A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model and important issues related to background updating for visual surveillance are discussed.
Abstract: Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues related to background updating for visual surveillance are discussed. Experimental results on surveillance examples, such as unloaded packages and unattended objects, are presented by showing those objects as short-term background layers.

49 citations

01 Jan 2007
TL;DR: A new traffic surveillance system that works without prior, explicit camera calibration, and has the ability to perform surveillance tasks in real time is proposed.
Abstract: As digital cameras and powerful computers have become wide-spread, the number of applications using vision techniques has increased significantly. One such application that has received significant attention from the computer vision community is traffic surveillance. We propose a new traffic surveillance system that works without prior, explicit camera calibration, and has the ability to perform surveillance tasks in real time. Camera intrinsic parameters and its position with respect to the ground plane were derived using geometric primitives common to any traffic scene. We use optical flow and knowledge of camera parameters to detect the pose of a vehicle in the 3D world. This information is used in a model-based vehicle detection and classification technique employed by our traffic surveillance application. The object (vehicle) classification uses two new techniques − color contour based matching and gradient based matching. Our experiments on several real traffic video sequences demonstrate good results for our foreground object detection, tracking, vehicle detection and vehicle speed estimation approaches.

39 citations

Proceedings ArticleDOI
TL;DR: A quality assessment method (MAD: Most Apparent Distortion) is presented which attempts to explicitly model these two separate strategies, local luminance and contrast masking and changes in the local statistics of spatial-frequency components, which are used to estimate appearance-based perceived distortion in low-quality images.
Abstract: The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences; extracting image structure/information). In this paper, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method (MAD: Most Apparent Distortion) which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.

38 citations