基于阴影检测的HSV空间自适应背景模型的车辆追踪检测

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

在交通监控中 ,要进行车辆的检测、车流量统计、实时追踪、车速测定等工作 ,而如何从复杂的背景中分割运动物体是至关重要的一步 ,目前采用的典型方法是背景相减方法 .为了对运动车辆进行准确快速的检测 ,在研究了目前存在的各种方法之后 ,提出了一种新的基于阴影检测的 HSV空间自适应背景模型的车辆追踪检测算法 ,并将其应用于运动物体的分割 ,同时给出了具体的试验结果 .该方法之所以不在传统的 RGB空间实现 ,而在 HSV空间实现 ,因为 HSV空间可以提供更丰富的颜色信息 .运行试验结果表明 ,该方法准确率高 ,适应性强 ,运算速度快 ,兼具灵活性 。

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.