Journal ArticleDOI
Detection and classification of vehicles
TLDR
Algorithm for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence is presented.Abstract:
This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user.read more
Citations
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Journal ArticleDOI
A Review of Computer Vision Techniques for the Analysis of Urban Traffic
TL;DR: A comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions is presented.
Journal ArticleDOI
Automatic traffic surveillance system for vehicle tracking and classification
TL;DR: Experimental results show that the proposed automatic traffic surveillance system is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
Journal ArticleDOI
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network
TL;DR: This paper introduces sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data and proposes a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images.
Journal ArticleDOI
Vehicle Detection Using Normalized Color and Edge Map
TL;DR: Zhang et al. as discussed by the authors proposed a new color transform model to find important "vehicle color" for quickly locating possible vehicle candidates, and three important features including corners, edge maps, and coefficients of wavelet transforms, are used for constructing a cascade multichannel classifier.
Journal ArticleDOI
Traffic accident prediction using 3-D model-based vehicle tracking
TL;DR: A probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed and the effectiveness of the proposed algorithms is shown.
References
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Proceedings ArticleDOI
Moving target classification and tracking from real-time video
TL;DR: An end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to image-based properties, and then robustly tracking them is described.
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Road traffic sign detection and classification
TL;DR: The algorithm described in this paper takes advantage of color thresholding to segment the image and shape analysis to detect the signs and uses a neural network for the classification.
Proceedings ArticleDOI
A real-time computer vision system for measuring traffic parameters
TL;DR: This paper describes the feature-based tracking approach for the task of tracking vehicles under congestion, a real-time implementation using a network of DSP chips, and experiments of the system on approximately 44 lane hours of video data.
Proceedings ArticleDOI
Towards robust automatic traffic scene analysis in real-time
Daphne Koller,Joseph Weber,Tim Huang,Jitendra Malik,Gary H. Ogasawara,Bhaskar D. Rao,Stuart Russell +6 more
TL;DR: Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled the authors to develop a system for detailed, reliable traffic scene analysis and preliminary results show that near real-time performance can be achieved without further improvements.