Abstract
Foreground detection plays an important role in the traffic surveillance applications, especially in urban intersections. Background subtraction is an efficient approach to segment the background and foreground with static cameras from video sensor networks. But when modelling the background, most statistical techniques adjust the learning rate only based on the changes from video sequences, which is a crucial parameter controlling the updating speed. This causes a slow adaptation to sudden environmental changes. For example, a stopped car fuses into background before moving again, and it lowers the segmentation performance. This paper proposes an efficient way to address the problem by accounting for the physical world signal in traffic junctions. It assigns an adaptive learning rate to each pixel by integrating traffic light signal obtained from sensor networks. Combined with abundant physical world signals, background subtraction method is able to adapt itself to the outside world changes instantly. We test our approach in real urban traffic intersection; experimental results show that the new method increases the accuracy of detection and has a promising future.
Highlights
Intelligent video surveillance, aiming at making traffic more intelligent and decreasing the amount of vehicle accidents, is a well-studied subject area with both existing application systems and new approaches still being developed
Because some of video-based traffic monitoring systems include high-level description of both cars and their behaviours, continuous tracking result is significant to the high-level processing
We present a novel method that utilises traffic light signal to enhance the performance of background subtraction, while existing methods use only image information to model and update the reference background
Summary
Intelligent video surveillance, aiming at making traffic more intelligent and decreasing the amount of vehicle accidents, is a well-studied subject area with both existing application systems and new approaches still being developed Among this area, detecting objects at the intersection is one of the most significant focuses in typical intelligent transportation systems (ITS) applications and the basis of high-level processing. In a traffic junction scene, vehicles always encounter congestion and stop-and-go when there is a red light At this time, a reasonable learning rate becomes more significant. This paper focuses on how to adjust learning rate according to the real-time and accurate physical world signals from other sensors. We select the more common traffic light as the external signal to improve the results of foreground detection.
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