Publication | Open Access
Robust techniques for background subtraction in urban traffic video
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2004
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Motion DetectionMachine VisionImage AnalysisEngineeringPattern RecognitionVideo ProcessingEye TrackingBackground ModelAdaptive Median FilteringVideo Content AnalysisComputer ScienceBackground SubtractionVideo SurveillanceRobust TechniquesComputer VisionMotion Analysis
Identifying moving objects from a video sequence is a fundamental and critical task in many computer‑vision applications, and background subtraction—detecting objects that differ from a background model—is a common approach that must be robust to illumination changes, avoid detecting non‑stationary background objects such as swinging leaves, rain, snow, and shadows cast by moving objects, and react quickly to background changes such as vehicle start‑and‑stop events. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.