Tracking Ground Vehicles in Heavy-traffic Video by Grouping Tracks of Vehicle Corners
22 Oct 2007-pp 396-399
TL;DR: This work proposes an algorithm that does not need background information to track vehicles in heavy-traffic video, and shows the effectiveness of the proposed algorithm under heavy congestion.
Abstract: Tracking vehicles in heavy-traffic video is a challenging problem. It is hard for algorithms based on background extraction to work well. We propose an algorithm that does not need background information. In this algorithm, vehicle corners are detected and tracked, and then grouped into vehicles. Experiments show the effectiveness of the proposed algorithm under heavy congestion.
Citations
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Patent•
05 Dec 2008TL;DR: A system and method for detecting and tracking one or more vehicles using a system for obtaining two-dimensional visual data depicting traffic flow on a road is disclosed in this paper, where stable features or unstable features are classified based on whether each feature is on the frontal face of a vehicle close to the road plane.
Abstract: A system and method for detecting and tracking one or more vehicles using a system for obtaining two-dimensional visual data depicting traffic flow on a road is disclosed. In one exemplary embodiment, the system and method identifies groups of features for determining traffic data. The features are classified as stable features or unstable features based on whether each feature is on the frontal face of a vehicle close to the road plane. In another exemplary embodiment, the system and method identifies vehicle base fronts as a basis for determining traffic data. In yet another exemplary embodiment, the system and method includes an automatic calibration procedure based on identifying two vanishing points
96 citations
01 Nov 2008
TL;DR: A time-spatial imagery based algorithm is proposed to estimate the road status from the video and the experimental results show that the time-Spatial method is robust in complex lighting and traffic environment.
Abstract: This paper presents a novel approach to detect traffic congestion on roads in a natural open world scene observed from TV cameras placed on poles or buildings. In this system, a time-spatial imagery based algorithm is proposed to estimate the road status from the video. The experimental results on real road traffic congestion estimation show that the time-spatial method is robust in complex lighting and traffic environment. The detailed algorithm and the comparison results are given in the paper.
35 citations
Cites background from "Tracking Ground Vehicles in Heavy-t..."
...In our system, the surveillance CCD camera is installed in a relatively far distance from a highway and the vehicles are visualized as small objects with minimum detail on their geometrical model....
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06 Nov 2009
TL;DR: The first steps taken in search of a solution that uses public video streams available on the Internet to address the increasing need for monitoring transportation networks with the intent of returning added value to the community are reported on.
Abstract: This paper reports on the first steps taken in search of a solution that uses public video streams available on the Internet to address the increasing need for monitoring transportation networks with the intent of returning added value to the community, either by allowing a better understanding of the network and its needs or by feeding applications with real-time information for various purposes, such as simulation, decision-making support and updated route guidance. After the introduction of the field, we present our findings from a survey that briefly describes several works with related studies and explain the algorithms that can be adopted to get relevant information from video streams. This is followed by an analysis of the issues that may arise and the best ways to address them. Next it reports on the results achieved so far, draws some conclusions on what has been done and suggests the next steps of our research.
17 citations
Cites background from "Tracking Ground Vehicles in Heavy-t..."
...Also, targeted at heavily congested traffic situations, [13] proposes an algorithm that detects and tracks vehicle corners and then groups them into vehicles....
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...In [13], instead, the suggested technique...
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...A different approach, presented in [13], does not require the background estimation, but instead it detects information about the corners of the vehicles....
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01 Dec 2016
TL;DR: Experimental results show that the proposed vision-based approach to accomplish automatic detection and speed measuring of vehicles leads to more reliable detections and more accurate speed estimations as compared to competing algorithms.
Abstract: A new vision-based approach to accomplish automatic detection and speed measuring of vehicles is proposed in this paper. In the proposed method, a cascade classifier, based on Haar features, is trained on frontal view of vehicles and deployed for vehicle detection. A fast and accurate foreground segmentation algorithm is proposed to distinguish moving vehicles from the background and prune detection results. The refined detection result in each individual frame is aggregated with tracking results based on a combination of Kalman filter and Munkres assignment algorithm. To accomplish an accurate and real-time speed measurement, an efficient sub-pixel matching technique in conjunction with stereo-vision framework is applied to calculate per-frame displacements of vehicles. Experimental results show that the proposed method leads to more reliable detections and more accurate speed estimations as compared to competing algorithms.
8 citations
Cites methods from "Tracking Ground Vehicles in Heavy-t..."
...Looking at literature on vision-based traffic monitoring, we encounter many methods for dealing with vehicle detection and tracking, and consequently speed control or traffic analysis in highways or intersections....
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31 Mar 2009
TL;DR: This paper introduces background extractions with self-adaptive update algorithm and puts forward an improved algorithm based on histogram statistic combining with multi-frame average that avoids the image trail-blur phenomena using pure multi- frame method on traffic jam, and has relative low computation complexity comparing with the hybrid Gauss model.
Abstract: The paper introduces background extractions with self-adaptive update algorithm and puts forward an improved algorithm based on histogram statistic combining with multi-frame average. It avoids the image trail-blur phenomena using pure multi-frame method on traffic jam, and has relative low computation complexity comparing with the hybrid Gauss model. It can run on the TI DM642 DSP hardware platform, the experimental image shows the fast extraction speed and gets good background image.
6 citations
References
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17 Jun 1997
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.
Abstract: For the problem of tracking vehicles on freeways using machine vision, existing systems work well in free-flowing traffic. Traffic engineers, however, are more interested in monitoring freeways when there is congestion, and current systems break down for congested traffic due to the problem of partial occlusion. We are developing a feature-based tracking approach for the task of tracking vehicles under congestion. Instead of tracking entire vehicles, vehicle sub-features are tracked to make the system robust to partial occlusion. In order to group together sub-features that come from the same vehicle, the constraint of common motion is used. In this paper we describe the system, a real-time implementation using a network of DSP chips, and experiments of the system on approximately 44 lane hours of video data.
545 citations
"Tracking Ground Vehicles in Heavy-t..." refers background in this paper
...For vehicles, corner features are proved to be effective [1]....
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20 Jun 2005
TL;DR: A concept called the relative height constraint is derived that makes it possible to estimate the 3D height of feature points on the vehicles from a single camera, a key part of the technique's ability to successfully segment and track vehicles at low angles.
Abstract: We present a novel method for visually monitoring a highway when the camera is relatively low to the ground and on the side of the road. In such a case, occlusion and the perspective effects due to the heights of the vehicles cannot be ignored. Features are detected and tracked throughout the image sequence, and then grouped together using a multilevel homography, which is an extension of the standard homography to the low-angle situation. We derive a concept called the relative height constraint that makes it possible to estimate the 3D height of feature points on the vehicles from a single camera, a key part of the technique. Experimental results on several different highways demonstrate the system's ability to successfully segment and track vehicles at low angles, even in the presence of severe occlusion and significant perspective changes.
113 citations
"Tracking Ground Vehicles in Heavy-t..." refers background in this paper
...Projection equalizers are applied for this conversion, and a number of improvements have been made [4], [5]....
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TL;DR: A novel method for visually monitoring a highway automatically when the camera is relatively low to the ground and on the side of the road, overcomes a limitation of commercially available machine vision-based traffic-monitoring systems that are used in many intelligent transportation systems (ITS) applications.
Abstract: A novel method is presented for visually monitoring a highway automatically when the camera is relatively low to the ground and on the side of the road In such a case, occlusion and the perspective effects that are due to the heights of the vehicles cannot be ignored By using a single camera, the system automatically detects and tracks feature points throughout the image sequence, estimates the 3-D world coordinates of the points on the vehicles, and groups those points together to segment and track the individual vehicles Experimental results on different highways demonstrate the ability of the system to segment and track vehicles even in the presence of severe occlusion and significant perspective changes By handling perspective effects, the approach overcomes a limitation of commercially available machine vision-based traffic-monitoring systems that are used in many intelligent transportation systems (ITS) applications Researchers are targeting this system as a step toward a next-generation ITS sensor for automated traffic analysis
39 citations
"Tracking Ground Vehicles in Heavy-t..." refers background in this paper
...Projection equalizers are applied for this conversion, and a number of improvements have been made [4], [5]....
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24 Oct 2001
TL;DR: A new feature-based vehicle tracking system using trajectory matching, which extracts corner features of the vehicle and tracks the features using linear Kalman filtering, where features from the same vehicle are grouped together.
Abstract: This paper describes a new feature-based vehicle tracking system using trajectory matching, which extracts corner features of the vehicle and tracks the features using linear Kalman filtering, where features from the same vehicle are grouped together. We also propose a new grouping algorithm using trajectory matching to make our tracking system robust enough for segmenting different vehicles in the congested traffic situation. The proposed system has demonstrated good performance for crossway traffic video sequences.
21 citations
"Tracking Ground Vehicles in Heavy-t..." refers methods in this paper
...X. Wang is with the Department of Electronical Engineering, Tsinghua University, Beijing, 100084 China (phone: (8610) 6278-1378; fax: (8610) 6277-3837; e-mail: wangxq_ee@tsinghua.edu.cn). algorithm is developed using trajectory matching [6]....
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05 Jan 2005
TL;DR: This work presents a vehicle tracking algorithm based on the KLT feature tracker which exploits a Kohonen Self Organizing Map (SOM) to drastically reduce tracking errors arising from occlusions, thus increasing the overall robustness of the system.
Abstract: Traffic monitoring systems based on image and sequence analyses are widely employed in Intelligent Transportation Systems (ITS's) in order to analyze traffic parameters and statistics. To this purpose, tracking objects is often needed. However, occlusions can mislead a vehicle tracking system based on a single camera, thus resulting in tracking errors. In this work we present a vehicle tracking algorithm based on the KLT feature tracker which exploits a Kohonen Self Organizing Map (SOM) to drastically reduce tracking errors arising from occlusions, thus increasing the overall robustness of the system. Our method has been implemented in a real-time traffic monitoring system that has been working on daily urban traffic scenes. The experimental results we present assess the effectiveness of our approach even in the presence of quite congestioned traffic situations.
9 citations
"Tracking Ground Vehicles in Heavy-t..." refers methods in this paper
...In some systems, feature tracking is used to improve the algorithms based on background extraction, by correcting the split and merging errors caused by occlusion [2], [3]....
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