AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement Learning

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2023-03-03 DOI:10.1145/3585316
Abdulrahman Fahim, E. Papalexakis, S. Krishnamurthy, Amit K. Roy Chowdhury, L. Kaplan, T. Abdelzaher
{"title":"AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement Learning","authors":"Abdulrahman Fahim, E. Papalexakis, S. Krishnamurthy, Amit K. Roy Chowdhury, L. Kaplan, T. Abdelzaher","doi":"10.1145/3585316","DOIUrl":null,"url":null,"abstract":"Steerable cameras that can be controlled via a network, to retrieve telemetries of interest have become popular. In this paper, we develop a framework called AcTrak, to automate a camera’s motion to appropriately switch between (a) zoom ins on existing targets in a scene to track their activities, and (b) zoom out to search for new targets arriving to the area of interest. Specifically, we seek to achieve a good trade-off between the two tasks, i.e., we want to ensure that new targets are observed by the camera before they leave the scene, while also zooming in on existing targets frequently enough to monitor their activities. There exist prior control algorithms for steering cameras to optimize certain objectives; however, to the best of our knowledge, none have considered this problem, and do not perform well when target activity tracking is required. AcTrak automatically controls the camera’s PTZ configurations using reinforcement learning (RL), to select the best camera position given the current state. Via simulations using real datasets, we show that AcTrak detects newly arriving targets 30% faster than a non-adaptive baseline and rarely misses targets, unlike the baseline which can miss up to 5% of the targets. We also implement AcTrak to control a real camera and demonstrate that in comparison with the baseline, it acquires about 2× more high resolution images of targets.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Cyber-Physical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3585316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Steerable cameras that can be controlled via a network, to retrieve telemetries of interest have become popular. In this paper, we develop a framework called AcTrak, to automate a camera’s motion to appropriately switch between (a) zoom ins on existing targets in a scene to track their activities, and (b) zoom out to search for new targets arriving to the area of interest. Specifically, we seek to achieve a good trade-off between the two tasks, i.e., we want to ensure that new targets are observed by the camera before they leave the scene, while also zooming in on existing targets frequently enough to monitor their activities. There exist prior control algorithms for steering cameras to optimize certain objectives; however, to the best of our knowledge, none have considered this problem, and do not perform well when target activity tracking is required. AcTrak automatically controls the camera’s PTZ configurations using reinforcement learning (RL), to select the best camera position given the current state. Via simulations using real datasets, we show that AcTrak detects newly arriving targets 30% faster than a non-adaptive baseline and rarely misses targets, unlike the baseline which can miss up to 5% of the targets. We also implement AcTrak to control a real camera and demonstrate that in comparison with the baseline, it acquires about 2× more high resolution images of targets.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
AcTrak:使用强化学习控制可操纵监控摄像头
可以通过网络控制的可操纵摄像机,用于检索感兴趣的遥测仪,已经变得流行起来。在本文中,我们开发了一个名为AcTrak的框架,以使相机的运动自动化,从而在(a)放大场景中的现有目标以跟踪其活动和(b)缩小以搜索到达感兴趣区域的新目标之间进行适当切换。具体而言,我们寻求在这两项任务之间实现良好的权衡,即,我们希望确保新目标在离开场景之前被摄像机观察到,同时也要频繁地放大现有目标,以监控其活动。存在用于操纵摄像机以优化某些目标的先验控制算法;然而,据我们所知,没有人考虑过这个问题,并且在需要跟踪目标活动时表现不佳。AcTrak使用强化学习(RL)自动控制相机的PTZ配置,以在给定当前状态的情况下选择最佳相机位置。通过使用真实数据集的模拟,我们发现AcTrak检测新到达的目标的速度比非自适应基线快30%,并且很少错过目标,而基线可能错过高达5%的目标。我们还实现了AcTrak来控制真实的相机,并证明与基线相比,它可以获得大约2倍多的目标高分辨率图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
期刊最新文献
A Comprehensive Threat Modelling Analysis for Distributed Energy Resources Carving out Control Code: Automated Identification of Control Software in Autopilot Systems Cooperative Driving of Connected Autonomous Vehicles using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning On Cyber-Physical Fault Resilience in Data Communication: A Case From A LoRaWAN Network Systems Design
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1