The Theory and Method of Data Acquisition of Mixed Traffic Popular People and Nonmotor Vehicles Based on Image Processing

In road mixed traffic, pedestrians and nonmotor vehicles have a great impact on the driving of motor vehicles. This kind of influence not only threatens the road traffic safety but also leads to the increase of delay and the decrease of traffic capacity. The purpose of this paper is to study the theory and method of data acquisition of mixed traffic popular people and nonmotor vehicles based on image processing technology. Aiming at the problem that the basic state space model solves the phenomenon of “failure” such as mutual interference between mixed objects, this paper proposes a KF tracking model based on a fuzzy matching method to realize the effective and accurate tracking of mixed traffic objects. The experimental results show that, after extracting the morphological features of the detected pedestrian and nonmotor vehicle images and using the method of pattern recognition to classify, recognize, and count the mixed traffic objects, through the comparison of the two trajectory lines, we can see that the tracking accuracy of the algorithm is high under the mutual interference of pedestrian and nonmotor vehicle. Excluding the detection error, the pedestrian tracking error is less than 10 pixels, the average error is 2.366 pixels, the maximum error of nonmotor vehicle tracking is 19 pixels, and the average error is 2.5 pixels.