[1]谢家阳,王行健,史治国,等.动态云台摄像机无人机检测与跟踪算法[J].智能系统学报,2021,16(5):858-869.[doi:10.11992/tis.202103032]
 XIE Jiayang,WANG Xingjian,SHI Zhiguo,et al.Drone detection and tracking in dynamic pan-tilt-zoom cameras[J].CAAI Transactions on Intelligent Systems,2021,16(5):858-869.[doi:10.11992/tis.202103032]
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动态云台摄像机无人机检测与跟踪算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年5期
页码:
858-869
栏目:
吴文俊人工智能技术发明奖一等奖
出版日期:
2021-10-05

文章信息/Info

Title:
Drone detection and tracking in dynamic pan-tilt-zoom cameras
作者:
谢家阳1 王行健1 史治国1 吴均峰1 陈积明1 陈潜2 王滨3
1. 浙江大学 信息学部,浙江 杭州 310027;
2. 上海无线电设备研究所,上海 200090;
3. 杭州海康威视数字技术股份有限公司,浙江 杭州 310052
Author(s):
XIE Jiayang1 WANG Xingjian1 SHI Zhiguo1 WU Junfeng1 CHEN Jiming1 CHEN Qian2 WANG Bin3
1. Faculty of Information Technology, Zhejiang University, Hangzhou 310027, China;
2. Shanghai Radio Equipment Research Institute, Shanghai 200090, China;
3. Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310052, China
关键词:
反无人机运动目标检测背景运动补偿目标识别目标跟踪深度学习机器视觉卷积神经网络
Keywords:
anti-UAVmoving target detectionbackground motion compensationobject recognitionobject trackingdeep learningcomputer visionconvolutional neural network
分类号:
TP391.4
DOI:
10.11992/tis.202103032
摘要:
为应对小型无人机的黑飞、滥飞对个人隐私、公共安全造成的威胁,本文采用高清云台摄像机定点巡航的方式对近地动态复杂背景中的无人机进行检测与跟踪,并提出了一种适用于动态云台摄像机的闭环无人机检测与跟踪算法,包含检测与跟踪两种模式。在检测模式下,本文设计了一种基于运动背景补偿的运动目标检测算法来提取分类候选区域,然后利用基于神经网络结构搜索得到的轻量级卷积神经网络对候选区域进行分类识别,可在不缩小高清视频图像的条件下实现无人机检测;在跟踪模式下,本文提出了一种结合卡尔曼滤波的局部搜索区域重定位策略改进了核相关滤波跟踪算法,使之在高清云台伺服追踪过程中仍能对目标进行快速稳定的跟踪;为将检测模式与跟踪模式结合在闭环框架中,本文还提出了一种基于检测概率和跟踪响应图状态的自适应检测与跟踪切换机制。实验表明,本文算法可应用于定点巡航状态的高清云台摄像机,实现近地复杂动态背景中无人机的实时准确检测、识别与快速跟踪。
Abstract:
To cope with the threat to personal privacy and public safety caused by illegal uses of small unmanned aerial vehicles, we propose a closed-loop UAV detection and tracking algorithm suitable for dynamic PTZ camera, which uses the fixed-point cruise mode of high-definition PTZ camera to detect and track unmanned aerial vehicles in the near-earth dynamic complex background, including two modes of detection and tracking. In the detection mode, this paper designs a moving target detection algorithm based on moving background compensation to extract the classification candidate areas, and then uses the lightweight convolutional neural network based on neural network structure search to classify and identify the candidate areas, which can realize UAV detection without reducing the high-definition video image; In the tracking mode, this paper proposes a local search area relocation strategy combined with Kalman filter to improve the tracking algorithm of kernel correlation filter, so that it can still track the target quickly and stably in the servo tracking process of HD PTZ; In order to combine detection mode and tracking mode in a closed-loop framework, this paper also proposes an adaptive detection and tracking switching mechanism based on detection probability and tracking response graph state. Experiments show that the proposed algorithm can be applied to high-definition PTZ cameras in a fixed-point cruise state, and realize real-time accurate detection, recognition and fast tracking of UAV in complex dynamic background near the ground.

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备注/Memo

备注/Memo:
收稿日期:2021-03-23。
基金项目:国家自然科学基金项目(62088101,61772467)
作者简介:谢家阳,博士研究生,主要研究方向为计算机视觉、反无人机系统、深度学习、目标检测与跟踪;王行健,硕士研究生,主要研究方向为计算机视觉、深度学习、目标检测与跟踪;史治国,教授、博士生导师,主要研究方向为研究方向为信号处理及定位应用,物联网。IET Fellow、IEEE Network编委、IET Communications编委、Journal of The Franklin Institute编委,2020年获中国人工智能学会技术发明一等奖,2015年获教育部科技进步一等奖,2012年获浙江省科学技术二等奖。主持国家重点研发项目、国家自然科学基金项目、浙江省重点研发计划项目等多项,获授权发明专利80余项。发表学术论文100余篇
通讯作者:史治国.E-mail:@zju.edu.cn
更新日期/Last Update: 1900-01-01