基于改进光流特征的运动目标跟踪

在城市智能视频监控中需要对运动目标进行实时跟踪，针对传统的运动目标检测中出现的跟踪目标易丢失、跟踪率低、实时性差等问题，提出一种基于改进光流特征的运动目标跟踪检测方法，对运动行人目标进行跟踪。该方法首先采用改进的Vibe运动背景建模法对视频中存在的运动行人进行检测，再将Shi-Tomasi角点检测与LK光流法进行结合，将角点检测结果融入到LK光流法中，并对检测到的角点进行运动光流特征提取，最后通过卡尔曼滤波对出现的行人进行预测跟踪，采用匈牙利最优匹配算法实现对运动目标的持续匹配以及对运动目标的跟踪。仿真结果表明，本文提出的方法能够对视频中出现的运动目标进行检测跟踪，具有较好的识别效果，且检测效率得到提高。 

Moving Target Tracking Based on Improved Optical Flow Characteristics

In the city intelligent video monitoring, it is necessary to track the moving object in real time. Aiming at the problems of the traditional moving object detection, for example, the target is easy to lose, low tracking rate and poor real-time performance, a moving object tracking detection method based on the improved optical flow characteristics is proposed to track the moving pedestrian object. Firstly, the improved Vibe moving background modeling method is used to detect the moving pedestrian in the video. Then the Shi-Tomasi corner detection and LK optical flow method are combined. The corner detection results are integrated into LK optical flow method, and the moving optical flow features of the detected corner points are extracted. Finally, Kalman filter is used to predict and track the pedestrians, and the Hungarian optimal matching algorithm is used to achieve continuous matching of moving objects and tracking of moving objects. The simulation results show that the proposed method can detect and track the moving objects in the video, and it has better recognition effect, and the detection efficiency is improved.