Adaptive HSV Color Background Modeling for Real-time Vehicle Tracking with Shadow Detection in Traffic Surveillance

Real time segmentation of moving objects in image sequence is a crucial step in traffic surveillance which include many different sub modules such as vehicle detection, vehicle statistic, real time tracking, speed measurement, etc. A typical method is background subtraction. Many background models have been introduced to deal with different problems at present. In the paper, we propose an adaptive HSV color background model with shadow detection to segment moving objects. We propose to operate in the Hue Saturation Value (HSV) color space, instead of the traditional RGB space, and show that it provides a better use of the color information, and naturally incorporates gray level only processing. At each instant, the system constructs three Gauss distribution for a pixel and maintains an updated background model, and a list of occluding regions that can then be tracked. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in adaptive color background model. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. The details of the algorithm are outlined and the experimental results are shown and evaluated. The results show that this algorithm combines the advantages of veracity and of runtime, and fit for real time detection.