Realization of background model in vehicle detection
Abstract: With the development of intelligent transportation technology, traffic detection in intelligent transportation system has become an important issue in the application of computer vision technology. Vehicle detection and tracking in sequence images play a key role in the field of intelligent transportation. Common methods of vehicle detection include the difference method based on frames, optical flow method and the difference method based on background. In order to solve the problem that the vehicle detection method based on background difference is susceptible to traffic conditions, an adaptive background model based on interval distribution is first established, and then an improved background update algorithm is used to selectively update the established background model. Experimental results show that the algorithm has good background extraction and update effects in complex traffic environments such as traffic jams or temporary parking. Compared with the classic algorithm, the vehicle detection algorithm has improved in real-time and accuracy. The background-based difference method can solve the problems based on the inter-frame difference method and the optical flow method, and the calculation is simple, but the background is easily affected by the traffic environment and light intensity, and the ideal background is not easy to obtain. Therefore, the adaptive background model of environmental changes It plays a very important role in the accuracy of moving vehicle detection. 1 Algorithm description Algorithm (Algorithm) is a series of clear instructions to solve the problem, the algorithm represents a systematic method to describe the strategy mechanism to solve the problem. In other words, you can get the required output within a limited time for a certain input. If an algorithm is flawed or not suitable for a problem, executing this algorithm will not solve the problem. Different algorithms may use different time, space or efficiency to complete the same task. The pros and cons of an algorithm can be measured by space complexity and time complexity. Intelligent transportation system is currently the hotspot of research and development in the transportation field of the world and countries. The method based on background difference is a common method for detecting moving objects from video streams, which is the focus of current research. Due to the influence of traffic conditions, weather, light intensity and other factors, it is not easy to obtain an ideal background, especially in the case of traffic jams, slow vehicle movement or temporary parking, the background update rate is low. Figure 1 is a flowchart of vehicle detection. First, establish a fast adaptive background model based on interval distribution, and then use the improved background update algorithm based on ε-δ to selectively update the established background model, combined with threshold segmentation and morphological operations to achieve the extraction of moving vehicles. Experimental results show that the algorithm proposed in this paper has a good background extraction and update effect for complex traffic environments (traffic jams, very heavy traffic, slow traffic, traffic jams or temporary parking, etc.). Compared with classic algorithms, the algorithm Both real-time and accuracy have been improved. 2 Adaptive background model In order to solve the problem of vehicle detection accuracy, domestic and foreign scholars have done a lot of research on background modeling. Reference [4] uses the average value of the pixels of the last N frames in the video image as the background model. This method requires multiple Gaussian mixture distributions for frequently changing pixels when multiple moving targets or moving targets are slow to move. In order to reflect changes in background pixels. These methods require that there are no moving vehicles during the establishment of the background model and the background model takes a long time to build, which cannot meet the needs of practical applications. This paper proposes a simple and effective background model and update method. 2.1 Establishment of background model In the video image sequence, the distribution of pixel values ​​of each coordinate point can be counted, and the pixel value with a high frequency of occurrence can be set as the pixel value of the corresponding point in the background model. However, this method has a relatively large amount of calculation and has poor adaptability to the gradual change of light and background. After defining ui (x, y) and Ci (x, y), the detailed steps to build the background model are as follows: (1) Determine which interval the current pixel belongs to, set to i. (2) Calculate ui (x, y) and Ci (x, y). (3) According to Ci (x, y), the interval is classified from small to large. (4) Set ui (x, y) in the interval with the largest Ci (x, y) as the pixel value of the corresponding point in the background model Mt. (5) Repeat steps (1) to (4) for all pixels in each frame of the video stream. Laptop Laser Power Supply,Desktop Power Adapter,Computer Power Supply,Switching Desktop Power Adapter Ninghai Yingjiao Electrical Co., Ltd. , https://www.yingjiaoadapter.com
