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@ {Article

author = {Luis Unzueta and Marcos Nieto and Andoni Cortés and Javier Barandiaran and Oihana Otaegui and Pedro Sánchez },

title = {Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification },

year = {2012-06-01 },

keys = {Computer vision, tracking, 3D reconstruction, traffic image analysis, traffic information systems. },

pages = {527- 540 },

abstract = {In this paper we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time achieving good results even in challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road, rainy days and traffic jams, using only a single standard camera. We propose a robust adaptive multi-cue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First, the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. Then, it adds extra features derived from gradient differences, in order to improve the segmentation of dark vehicles with casted shadows, and removes headlights reflections on the road. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2D Kalman filter, and the complexity of a 3D volume estimation using Markov Chain Monte Carlo (MCMC) methods. Experimental results show that our method can count and classify in real time vehicles with a high performance under different environmental situations, comparable to those of inductive loop detectors (ILD). },

issn = {1524-9050 },

in = {IEEE Transactions on Intelligent Transportation Systems },