一种新的基于区域的高速公路多车辆跟踪方案

提出一种新的基于区域的高速公路多车辆跟踪方案,包括背景建模、目标识别、目标跟踪等过程。针对高速公路监控图像质量差和干扰信号强的特点,在常规的颜色混合高斯背景模型的基础上,提出一种新的基于扰动区域的高斯背景模型来消除强噪声和背景小幅度运动的影响,并在时间序列上通过Kalman滤波迭代加权算法实现背景模型的自适应性更新。该背景模型明显提高了背景分割的准确性和自适应性。提出了一种改进的非递归区域生长算法用以有效地实现多目标的识别,算法复杂度仅为O(n)。采用目标特征匹配和区域运动预测规则对多车辆进行实时跟踪和识别。实现了一个高速公路实时监控原型系统,运行结果表明,该跟踪方法不仅能准确跟踪和识别多目标,而且对道路环境和车辆运动方向具有很好的适应性和鲁棒性。

New region-based surveillance scheme for high-way multi-vehicle recognition and tracking

This paper provides a new region-based high-way multi-vehicle surveillance system scheme. The contribution of this system includes a modified adaptive Gaussian background model, a non-recursive region-growth algorithm for multi-vehicle recognition, and a color-based predictive rule for multi-vehicle tracking. Since the high-way images are usually strongly disturbed, a modified region-based Gaussian background model is presented in order to remove much heavy noise and small disturbing regions. To build this model, we first used several background imaging samples to get the disturbed regions by background subtraction technique, and then computed the statistic value of the size of disturbed regions as the parameters of this region-based Gaussian background model. In order to reduce the effect of daylight and be updated adaptively, we also applied a weighted iterative algorithm based on Kalman filter theory. A modified non-recursive region-growth algorithm is presented for multi-vehicle recognition whose computational complexity can be only O(n), and a color-based predictive rule is also proposed for multi-vehicle tracking which is very easy to implement. At last, the experimental results show that this system can achieve multi-vehicle recognition and tracking robustly and adaptively.