Vehicle Detection And Tracking Algorithm By Direction Detection

The detection and tracking of aerial targets is an important research field in the computer vision.In terms of road traffic monitoring,aerial photography has the advantage that a fixed camera is difficult to surpass.The UAV aerial image has a wide field of view and is easy to identify.Especially in the case of temporary emergency situations,the drone's flexible performance is better adapted to the rapidly changing scenes.In recent years,deep learning algorithms has a huge advantage of traditional methods.In the field of target detection,the deep learning method has made great progress.In the recall rate,precision and other indicators,deep learning has achieved a qualitative improvement.This paper uses the deep learning,and divides the detection and tracking problem of aerial vehicles into three parts: aerial vehicle target detection,aerial vehicle body direction detection,and aerial vehicle tracking.After that,we combine these three parts together.The specific approach is as follows:One: Train the target detection network for aerial vehicles.Since there is no aerial vehicle in the open target detection data set,this paper uses the newly labeled aerial vehicle data set to train the model based on the already-discovered target detection pre-training model,so that the model can be used to detect aerial photography Vehicle target.By using the R-FCN network model to detect,the recall rate reached 92.78%,and the accuracy rate reached 87.38%.Second: propose a direction detection network for the direction of the aerial vehicle body.This paper studies the problem of target rotation in aerial vehicle tracking,and design a convolutional neural network for detecting the direction of the vehicle body for the detected aerial vehicle target.The network output of the convolutional neural network is a 360-dimensional vector and normalized,corresponding to 0-359 degrees.The network output vector label is a normalized Gaussian function whose mean is equal to the vehicle's direction.After testing in the test set,when the predicted angular error does not exceed 10 degrees,the accuracy rate reaches 97.24%.Then use the vehicle body direction information in the tracking algorithm.Three: This article designed two sets of demonstration systems.The first set of systems mainly demonstrates aerial vehicle target detection and car body direction detection.The second set of system mainly demonstrates aerial vehicle target detection,direction detection and tracking.The target detection uses the R-FCN model,which achieves a detection rate of 3.5 frames per second on the i7-6700 HQ,GTX950 platform for images of 640×480 size.Direction detection reaches a detection rate of 500 frames per second.The experimental results show that this paper can complete target detection,vehicle orientation detection and target tracking tasks of aerial vehicles with high accuracy,and proves that the method has high practicability in the demonstration system.