Design And Implementation Of Vision Based Drone Intrusion Detection And Tracking System

The rapid development and extensive application of drones bring various conveniences,but also lead to frequent incidents of the 'black flying' and ' indiscriminate flying' of drones that may pose serious threats to public safety and personal privacy.So the demand for anti-drone becomes more and more intense.The research on anti-drone system is of great significance not only in public security control and safety in production but also anti-terrorism.Drone is a typical low slow small target.It has the characteristics of low altitude flight,ultra-low altitude flight,complex environment,slow flight,doppler effect is not obvious,small reflection area,for which it is difficult to detect the drones.But at present,there is no mature drone detection system in China.The vision based drone detection system has limited detection distance and accuracy for drone too.In order to meet the need of anti-drone system and cover the shortage of existing systems,this thesis designs a invasive drone detection and tracking system based on machine vision.The system deals with the video images collected by cameras with target detection and tracking algorithms so as to detect and track the invasive drones.And the following is the contents of the thesis.1.This thesis uses the classic gradient histogram feature and support vector machine classifier(HOG+SVM)algorithm to detect the drones and achieves great detection results.Through lots of experiments,the performance of HOG+SVM algorithm under different parameters is compared and analyzed,and the most suitable parameters for drone detection are obtained.And by using the feature Pyramid,the system can detect the small drones.Experiments show that the algorithm with the appropriate parameters and enough training has great performance,higher recall and precision,and meets the accuracy requirement of the system.2.The high resolution camera is used to real-time monitor the small drones in this system.Aiming at the problem that the detection speed of HOG+SVM algorithm in high-resolution images is low and can not meet the real-time requirement of the system,this thesis presents a improved algorithm combined with three-frame difference method and visual background extractor(ViBe)algorithm for moving target detection.The improved algorithm solves the shortcomings of three frame difference method and ViBe that three frame difference method will fail with camera jitter and ViBe appears ghost and improves the accuracy of moving object detection too.Then this thesis uses the HOG+SVM algorithm to detect drones in the region of interest obtained from the im-proved moving target detection algorithm.Experiments shows that the HOG+SVM algorithm combined with the improved moving target detection algorithm reduces the false detection rate and can meet the real-time require-ment of the system.3.Using multi-scale kernelized correlation filters(KCF)algorithm for small drone tracking.And an im-proved KCF algorithm based on kalman filter is proposed and implemented for the problem that the KCF algo-rithm is difficult to track the target with fast motion.The displacement of the target between the current frame and the next frame is predicted by kalman filtering,so the position of the drone in the next frame can be estimat-ed.And then this thesis uses the KCF algorithm to track in the estimated position.Experiments show that the improved KCF algorithm can track the target with fast motion,and tracking accuracy of algorithm is basically unchanged and can meet the real-time requirement of the system.4.This thesis designs the overall implementation of the system that initializing the tracking algorithm with the result of the detection algorithm and detecting when tracking failure and starting tracking after a drone is detected.The system uses the multi-thread method and chain queues to implement the sharing,reading,and real-time processing of images.In addition,this thesis also designs the client software of the system for the interaction between the user and the system.The user can control the camera and see the detection and tracking results through the client software.Finally,the function of this system is tested by lots of experiments.It is verified that the small drones can be detected and tracked by this system,the monitoring distance can up to 480 meters in the daytime,up to 180 meters at night,the detection accuracy is high and the real-time performance is great.