Vision-based Traffic Surveillance Research Articles

V-DaT: A Robust method for Vehicle Detection and Tracking

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

Accurate vehicle location and tracking algorithms based on improved kernelized correlation motion model and Kalman filter in intelligent transport surveillance system

Vision-based traffic surveillance, an indispensable part of intelligent transport system (ITS), has been widely studied over past few years. Due to the factors such as visual occlusion, illumination change and pose variation, it is a challenging task to develop effective and efficient models for vehicle detection and tracking in surveillance videos. Although plenty of existing related models have been proposed, many problems still need to be resolved. A novel improved kernelized correlation tracking algorithm based on Kalman filter and motion model is proposed to solve the tracking accuracy deteriorated by fast-moving and severe occlusion. Firstly, an adaptive search region location method is proposed, where the optimal position is estimated by the uncertainty theory of the motion model so as to obtain the optimal search window. Secondly, a confidence strategy for similarity measurement is calculated by using the sharpness of the correlation peak and the smoothness constraint. Finally, the optimal confidence is introduced to get an adaptive update model. A large number of simulation experiments show that our proposed algorithm has more robust performance and anti-interference ability than the traditional KCF algorithm, which is suitable for intelligent transport system application.

Automated Traffic Surveillance System with Aerial Camera Arrays Imagery: Macroscopic Data Collection with Vehicle Tracking

AbstractThe paper presents a novel computer vision-based traffic surveillance system capable of processing aerial imagery to track vehicles and their movements. The system uses a preprocessed 1-Hz ...

Full-Body Occlusion Handling and Density Analysis in Traffic Video-Surveillance Systems

Vision-based traffic surveillance systems are among the most reliable, inexpensive, and highly applicable methodologies for surveying traffic conditions. The implementation of these strategies, however, is limited under certain conditions, such as the presence of vehicle occlusions or poor illumination conditions that lead to either overcounted or under-counted traffic data. The proposed motion-based methodology is intended to overcome these limitations by using a new technique for full-body occlusion handling of vehicles. The methodology is based on five main steps: calibration, detection, tracking, counting, and occlusion handling. The proposed methodology was tested with various 30–min videos and 452 preidentified cases of occlusion. Preliminary results indicated that the proposed methodology was reliable and robust in providing traffic density analysis. Future work may rely on the extension of the proposed methodology to deal with the detection of vehicles moving toward multiple directions.

Detection and Classification of Highway Lanes Using Vehicle Motion Trajectories

Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types. Accompanying techniques for indexing and retrieval of anomalous trajectories are also derived. The predictive trajectory merge-and-split algorithm is used to detect partial or complete occlusions during object motion and incorporates a Kalman filter that is used to perform vehicle tracking. The resulting motion trajectories are modeled using variable low-degree polynomials. A K-means clustering technique on the coefficient space can be used to obtain approximate lane centers. Estimation bias due to vehicle lane changes can be removed using robust estimation techniques based on Random Sample Consensus (RANSAC). Through the use of nonmetric distance functions and a simple directional indicator, highway lanes can be classified into one of the following categories: entry, exit, primary, or secondary. Experimental results are presented to show the real-time application of this approach to multiple views obtained by an uncalibrated pan-tilt-zoom traffic camera monitoring the junction of two busy intersecting highways.

3-D reconstruction of a dynamic environment with a fully calibrated background for traffic scenes

Vision-based traffic surveillance systems are more and more employed for traffic monitoring, collection of statistical data and traffic control. We present an extension of such a system that additionally uses the captured image content for 3-D scene modeling and reconstruction. A basic goal of surveillance systems is to get a good coverage of the observed area with as few cameras as possible to keep the costs low. Therefore, the 3-D reconstruction has to be done from only a few original views with limited overlap and different lighting conditions. To cope with these specific restrictions we developed a model-based 3-D reconstruction scheme that exploits a priori knowledge about the scene. The system is fully calibrated offline by estimating camera parameters from measured 3-D-2-D correspondences. Then the scene is divided into static parts, which are modeled offline and dynamic parts, which are processed online. Therefore, we segment all views into moving objects and static background. The background is modeled as multitexture planes using the original camera textures. Moving objects are segmented and tracked in each view. All segmented views of a moving object are combined to a 3-D object, which is positioned and tracked in 3-D. Here we use predefined geometric primitives and map the original textures onto them. Finally the static and dynamic elements are combined to create the reconstructed 3-D scene, where the user can freely navigate, i.e., choose an arbitrary viewpoint and direction. Additionally, the system allows analyzing the 3-D properties of the scene and the moving objects.

Visual surveillance using deformable models of vehicles

This paper presents recent developments to a vision-based traffic surveillance system which relies extensively on the use of geometrical and scene context. Firstly, a highly parametrised 3-D model is reported, able to adopt the shape of a wide variety of different classes of vehicle (e.g. cars, vans, buses etc.), and its subsequent specialisation to a generic car class which accounts for commonly encountered types of car (including saloon, batchback and estate cars). Sample data collected from video images, by means of an interactive tool, have been subjected to principal component analysis (PCA) to define a deformable model having 6 degrees of freedom. Secondly, a new pose refinement technique using “active” models is described, able to recover both the pose of a rigid object, and the structure of a deformable model; an assessment of its performance is examined in comparison with previously reported “passive” model-based techniques in the context of traffic surveillance. The new method is more stable, and requires fewer iterations, especially when the number of free parameters increases, but shows somewhat poorer convergence. Typical applications for this work include robot surveillance and navigation tasks.