Vehicle Segmentation and Tracking in the Presence of Occlusions

Abstract

A novel method is presented for automatically 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. Using a single camera, the system automatically detects and tracks feature points throughout the image sequence, estimates the 3D world coordinates of the points on the vehicles, and groups those points together in order 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. The researchers are targeting this system as a step toward a next generation ITS sensor for automated traffic analysis.

A Multi-camera Approach to Vehicle Tracking Based on Features

“…The use of the second camera improves the previous work [5] [4] in several ways. First of all, it avoids estimating the feature heights, and thus does not rely on the foreground classifier [4], which is subject to lighting changes.…”

“…A good survey can be found in [5]. Among all the attempts, featurebased detection has emerged as a promising tool [1], because it can avoid the ambiguities caused by vehicle occlusions.…”

Vehicle Detection and Tracking Techniques : A Concise Review

“…An automatic unique visual-based expressway surveillance approach for segmenting and tracking vehicles during the image series with existence rigorous occlusion due to low-level floor position of camera on the roadside [52]. In this paper, the particular vehicles are detected, segmented and tracked in image sequence by assembling, bunching and approximating of the 3D world coordinates of vehicle's feature points.…”

Extension of Cellular Automata for Dynamic Vehicle Tracking

“…Failing to detect and resolve the presence of occlusion may lead to surveillance errors, including incorrect vehicle count, incorrect tracking of individual vehicles and incorrect classification of vehicle type on that road segment. However, occlusion detection and resolution are inherently complex, as they rely on priori vehicle features that would indicate whether a particular moving object consists of one or more than one vehicle [17,34]. If it is the latter case, then these features would have to provide a basis for differentiating which vehicle is which.…”