Research On Rapid Identification And Tracking Technology Of Specific Dynamic Small Target

Specific dynamic target recognition and tracking have important applications in video surveillance and other aspects.In recent years,the development of machine learning technology and convolutional neural networks has greatly promoted the development of feature extraction and image pattern recognition technology.This subject based on convolutional neural network technology,supplemented by traditional algorithms such as Kalman filtering and Hungarian is aimed at the rapid detection and recognition of specific small targets and the tracking of dynamic specific targets.The main research contents are as follows:The feature extraction algorithms commonly used in target detection are introduced.It mainly summarizes from the feature extraction speed and the applicable range,and points out that compared with the traditional feature extraction algorithm,the use of convolutional neural network for specific target extraction can have better feature extraction effect and faster speed and can be applied for the rapid identification process of specific small targets.A detector for static specific small targets based on convolutional neural network is established.Starting from the specific experimental purpose,a data set containing three categories of aircraft,birds and turbine engines is constructed for network training and detection,and the network's recognition of small targets is increased by optimizing the extraction position of the internal feature map of the network and adding new decision rules.By combining the Kalman filter and the Hungarian algorithm,a expansion of the detector for the static specific small target based on the convolutional neural network is completed,so that it can quickly identify and track the dynamic specific target in the video.Since the detector only detects static specific targets,which severely limits the scope of its use,this paper uses the Kalman filter and the detection results of the detector to predict the state of the next frame,and then uses Hungarian algorithm to performs state allocation to complete ID allocation and tracking of specific targets.Completed the design of the rapid identification and tracking system for the dynamic specific target,and quantitatively tested its effect.In the experiment,an NVIDIA GeForce GTX 1060 6GB GPU is selected for training detection and tracking process based on the convolutional neural network detector.Under this hardware condition,the experimentally established detector has a good detection effect for specific targets of small targets and normal size.It can detect in real time on the self-built detection data set and mAP@75 can reach 47.41.After joining the tracking process,the speed of the entire system can still meet the requirements of rapid detection and tracking.The average running speed can reach 0.1097s/frame,and the tracking process speed can reach 0.001577s/frame.The use of status and area information improves the common ID switch problem in the tracking process,achieves a good tracking effect,and has a wide range of application prospects.