Article

Tracking multiple targets using binary proximity sensors

Authors:

Jaspreet Singh,

Upamanyu Madhow,

Richard CagleyAuthors Info & Claims

IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks

Pages 529 - 538

https://doi.org/10.1145/1236360.1236427

Published: 25 April 2007 Publication History

Abstract

Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of such sensors can provide remarkably good target tracking performance. In this paper, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models for the trajectories yield respectable tracking performance.

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Cited By

Bassolillo SD’Amato ENotaro I(2025)A Consensus-Driven Distributed Moving Horizon Estimation Approach for Target Detection Within Unmanned Aerial Vehicle Formations in Rescue OperationsDrones10.3390/drones90201279:2(127)Online publication date: 9-Feb-2025
https://doi.org/10.3390/drones9020127
Coffin HAbraham ISartoretti GDillstrom TChoset H(2022)Multi-Agent Dynamic Ergodic Search with Low-Information Sensors2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9812037(11480-11486)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9812037
Doostmohammadian MTaghieh AZarrabi H(2021)Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.3135834(1-12)Online publication date: 2021
https://doi.org/10.1109/TASE.2021.3135834
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Index Terms

Tracking multiple targets using binary proximity sensors
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
        Vision for robotics
2. Mathematics of computing
  1. Discrete mathematics
    1. Combinatorics
      1. Enumeration
  2. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

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Published In

cover image ACM Conferences

IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks

April 2007

592 pages

ISBN:9781595936387

DOI:10.1145/1236360

General Chair:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign
,
Program Chairs:
Leo Guibas
Stanford University
,
Matt Welsh
Harvard University

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 April 2007

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IPSN07

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IPSN07: The 6th International Conference on Information Processing in Sensor Networks

April 25 - 27, 2007

Massachusetts, Cambridge, USA

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Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

Bassolillo SD’Amato ENotaro I(2025)A Consensus-Driven Distributed Moving Horizon Estimation Approach for Target Detection Within Unmanned Aerial Vehicle Formations in Rescue OperationsDrones10.3390/drones90201279:2(127)Online publication date: 9-Feb-2025
https://doi.org/10.3390/drones9020127
Coffin HAbraham ISartoretti GDillstrom TChoset H(2022)Multi-Agent Dynamic Ergodic Search with Low-Information Sensors2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9812037(11480-11486)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9812037
Doostmohammadian MTaghieh AZarrabi H(2021)Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.3135834(1-12)Online publication date: 2021
https://doi.org/10.1109/TASE.2021.3135834
Saito HHonda H(2019)Geometric Analysis of Estimability of Target Object Shape Using Location-Unknown Distance SensorsIEEE Transactions on Control of Network Systems10.1109/TCNS.2018.27978076:1(94-103)Online publication date: Mar-2019
https://doi.org/10.1109/TCNS.2018.2797807
Nourhan T.A. AHossam M.A. FHady A(2019)The Enhanced Probability Hypothesis Density-based Filter for Multitarget Tracking and Counting2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES)10.1109/NILES.2019.8909329(92-97)Online publication date: Oct-2019
https://doi.org/10.1109/NILES.2019.8909329
Al-Hameed AYounus SHussein AAlresheedi MElmirghani J(2019)Artificial Neural Network for LiDAL SystemsIEEE Access10.1109/ACCESS.2019.29334707(109427-109438)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2933470
Liu JWang CChen YSaxena NThuraisingham BEvans DMalkin TXu D(2017)VibWriteProceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security10.1145/3133956.3133964(73-87)Online publication date: 30-Oct-2017
https://dl.acm.org/doi/10.1145/3133956.3133964
Wang YLi WLu S(2016)The Capability of Error Correction for Burst-Noise Channels Using Error Estimating Code2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SAHCN.2016.7733022(1-9)Online publication date: Jun-2016
https://doi.org/10.1109/SAHCN.2016.7733022
Wang YSong LGu ZLi D(2016)IntenCT: Efficient Multi-Target Counting and Tracking by Binary Proximity Sensors2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SAHCN.2016.7732998(1-9)Online publication date: Jun-2016
https://doi.org/10.1109/SAHCN.2016.7732998
Xing TFang DChen XJin MZheng XZhao KZhang YLi MYang PQian CHan J(2015)Doppler Effect based Moving Target Detection Adaptive to SpeedProceedings of the 1st Workshop on Context Sensing and Activity Recognition10.1145/2820716.2820722(41-46)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1145/2820716.2820722
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