Robert R. Price

Detection of scene obstructions and persistent view changes in transportation camera systems

Unattended camera devices are increasingly being used in various intelligent transportation systems (ITS) for applications such as surveillance, toll collection, and photo enforcement. In these fielded systems, a variety of factors can cause camera obstructions and persistent view changes that may adversely affect their performance. Examples include camera misalignment, intentional blockage resulting from vandalism, and natural elements causing obstruction, such as foliage growing into the scene and ice forming on the porthole. In addition, other persistent view changes resulting from new scene elements of interest being captured, such as stalled cars, suspicious packages, etc. might warrant alarms. Since these systems are often unattended, it is often important to automatically detect such incidents early. In this paper, we describe innovative algorithms to address these problems. A novel approach that uses the image edge map to detect near-field obstructions without a reference image of the unobstructed scene is presented. A second algorithm that can be used to detect more generic obstructions and persistent view changes using a learned scene element cluster map is then discussed. Lastly, an approach to detect and distinguish persistent view changes from changes in the orientation of the fixed camera system is explained. Together, these algorithms can be useful in a variety of camera-based ITS.

Reference image-independent fault detection in transportation camera systems for nighttime scenes

Automated, unattended camera systems used for various transportation applications such as toll collection and photo enforcement need to capture high quality images in a variety of outdoor scenarios. In particular, they need to remain functional in low ambient illumination conditions (nighttime and cloudy day situations) to enable identification of objects or persons involved in incidents being monitored or extracting relevant information. Over time, for installed camera systems in the field, several problems can develop, such as external flash unit failures, focus drifts, and exposure issues. Thus, it is important to periodically monitor the nighttime images/videos taken by the camera system to ensure nominal functionality. However, due to constantly changing scene elements, it is not practical to compare a historical reference image with an identical scene against current camera output to detect problems. To address this, we present image quality metrics that can be extracted without a nominal reference image and can be used to characterize these problems. These can be incorporated into algorithms that can enable automated camera diagnostics for intelligent transportation systems.