Detection and Classification of Highway Lanes Using Vehicle Motion Trajectories

Abstract

Abstract-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.

A low-bandwidth camera sensor platform with applications in smart camera networks

“…Beyond the low-level object detection and tracking, there has been a consistent interest in modeling and predicting traffic patterns using information obtained from camera sensor networks. The goals of the application typically include path discovery [Kettnaker and Zabih 1999;Melo et al 2006], traffic statistics prediction [Guitton et al 2007;Tubaishat et al 2009], and accident detection [Kamijo et al 2000;Bramberger et al 2006], to name a few. A concrete example using CITRIC motes is presented in Shuai et al [2010].…”

IMM-Based Lane-Change Prediction in Highways With Low-Cost GPS/INS

“…Lane-change prediction is approached in the literature from many different points of view, most of them based on vision systems [2], [19], [20]. In [2], the authors make a comparison of previous research that distinguishes between driver intent inference and trajectory prediction.…”

Study the Effect of Threshold Value on Object Detection

“…In other terms, object detection and tracking in a video sequences have been one of many important problems in computer vision and have attracted more and more researchers working on it. Furthermore, moving object detection has been used for many computer vision applications, including recognition of traffic scenarios [1], supervision traffic flow [2], collision prediction of pedestrians [3], face detection [4], human-machine interaction [5], etc. While detecting and tracking, we need to analyze video sequences to detect and track target in each frame, to achieve monitoring and to master the dynamic variation of the moving objects in order to confirm their exact position.…”