Research on multi-target tracking method based on Kalman filter and deep learning

In harsh environments such as rain, snow, fog, and haze, the target perception capability of the optoelectronic system is seriously reduced. At the same time, the embedded image processing hardware platform completes high-definition video image preprocessing, target detection and other image processing tasks with slow response and time-consuming. The key video frame decoding proposed in this paper reduces the requirement of the tracker on the computing power of the system, and the image enhancement reduces the influence of the environment on the tracking effect. At the same time, the target tracking problem is converted into a "detection-prediction-tracking" problem. The detection model obtains the target position of the current video frame in real time, and the prediction model introduces the historical motion information of the target to predict the current position of the target. The tracker determines the target position of the detection model and the prediction model. Confidence tracking results are obtained after scoring. The experimental results show that the method can solve the influence of target deformation and occlusion and harsh environment on the tracking results to a certain extent, reduce the loss rate of tracking targets, and improve the accuracy and stability of tracking.