Pixel-based heterogeneous traffic measurement considering shadow and illumination variation

Abstract

This paper endeavors to develop a new background estimation model for foreground segmentation using traditional background subtraction technique. Particularly, incorporation of luminance- and pollution-controlling parameters enables the model to address illumination variation and tail backs from vehicles, respectively. To classify each foreground pixel, a new heuristic dynamic threshold-difference function is proposed for determining individual threshold. Moreover, newly presented Positive Negative Segmentation technique removes shadow considering its physical characteristics from the foreground. After shadow correction, impulse flow waves and aggregated pictorial speed are computed accordingly. Impulse flow waves are eventually rectified and cumulated into actual flow. Pictorial speed is converted into actual speed using calibration equation considering perspective error. To facilitate video-based traffic measurement, a user-friendly tool PARTS (Pixel-Based Heterogeneous Traffic Measurement) is developed which incorporates the whole system. The data collected at different roadway locations are compared with that generated by the tool. It shows 90 and 97% correlations in measuring flow and speed, respectively.

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