基于视觉机制的非结构环境下车速检测算法研究

Vehicle speed detection algorithm based on visual mechanism under unstructured environment

在非结构环境下,基于视频检测目标车速为目的的关键问题是:如何利用视频序列准确、高效地得到运动目标的实时信息。基于视觉感知机制的车速检测是一种新方法,该方法以视觉机制为启发,首先从图像中学习初级视皮层细胞超完备基;然后基于超完备基视觉计算模型,以神经元响应系数表征目标,并根据背景与目标响应系数差值与动态阈值的比较识别运动目标;通过迭代实现目标跟踪;在目标跟踪的基础上,根据不同视频序列中车辆特征匹配法得到车辆的位移,最后实现车速检测。实验结果表明,该算法能够在实时条件下检测实际道路场景的不同类型车辆速度,达到96.55%以上的准确率,并且与传统方法相比较提高了车速检测的准确性和鲁棒性。

The key issue of the purpose of detecting target vehicle speed based on video is how to obtain the accurate and efficient real-time information of moving targets using the video sequence in unstructured environment. Vehicle speed detection based on visual perception mechanism is a kind of new method. Visual mechanism inspired the method,first of all it should learn the over-complete characterization of primary visual cortex cells from nature images. Then it compared with the dynamic threshold to identify the moving target based on over-complete visual calculation model according to neuron response coefficient characterization and the difference of neuronal response coefficient between background images and goal images. Next,it achieved target tracking through the iterative method. Compared with the traditional method it got the displacement of the vehicle according to the matching method of characteristics in different video sequences on the basis of target tracking. Finally it could realize speed detection by this algorithm. Experimental results show that this algorithm can test the vehicle speed of different types in the actual road scene under real-time conditions and reach more than 96. 55% accuracy. The algorithm improves the vehicle speed detection in accuracy and robustness compared with the traditional method.