SenseFlow: An Experimental Study for Tracking People

The main challenges in large-scale people tracking are the recognition of people density in a specific area and tracking the people flow path. To address these challenges, we present SenseFlow, a lightweight people tracking system. SenseFlow utilises off-the-shelf devices which sniff probe requests periodically polled by user's smartphones in a passive manner. We demonstrate the feasibility of SenseFlow by building a proof-of-concept prototype and undertaking extensive evaluations in real-world settings. We deploy the system in one laboratory to study office hours of researchers, a crowded public area in city to evaluate the scalability and performance "in the wild", and four classrooms in the university to monitor the number of students. We also evaluate SenseFlow with varying walking speeds and different models of smartphones to investigate the people flow tracking performance.

[1]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[2]  T. Teixeira,et al.  A Survey of Human-Sensing : Methods for Detecting Presence , Count , Location , Track , and Identity , 2010 .

[3]  Antonio Fernández Caballero,et al.  Optical flow or image subtraction in human detection from infrared camera on mobile robot , 2010 .

[4]  Kevin Curran,et al.  A survey of active and passive indoor localisation systems , 2012, Comput. Commun..

[5]  M. Amaç Güvensan,et al.  On coverage issues in directional sensor networks: A survey , 2011, Ad Hoc Networks.

[6]  Hubert Klüpfel,et al.  Evacuation Dynamics: Empirical Results, Modeling and Applications , 2009, Encyclopedia of Complexity and Systems Science.

[7]  Mikkel Baun Kjærgaard,et al.  Mobile sensing of pedestrian flocks in indoor environments using WiFi signals , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[8]  Levent Demir Wi-Fi tracking : what about privacy , 2013 .

[9]  Antonio Fernández-Caballero,et al.  Optical flow or image subtraction in human detection from infrared camera on mobile robot , 2010, Robotics Auton. Syst..

[10]  Philipp Marcus,et al.  Estimating crowd densities and pedestrian flows using wi-fi and bluetooth , 2014, MobiQuitous.

[11]  Scott Bell,et al.  Human Spatial Behavior, Sensor Informatics, and Disaggregate Data , 2013, COSIT.

[12]  Julien Freudiger,et al.  How talkative is your mobile device?: an experimental study of Wi-Fi probe requests , 2015, WISEC.

[13]  Nebojsa Jojic,et al.  A Graphical Model for Audiovisual Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  M. Horowitz HUMAN SPATIAL BEHAVIOR. , 1965, American journal of psychotherapy.

[15]  Cecilia Mascolo,et al.  EmotionSense: a mobile phones based adaptive platform for experimental social psychology research , 2010, UbiComp.

[16]  A. B. M. Musa,et al.  Tracking unmodified smartphones using wi-fi monitors , 2012, SenSys '12.

[17]  Cristiano Premebida,et al.  Exploiting LIDAR-based features on pedestrian detection in urban scenarios , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[18]  Yusheng Ji,et al.  Passive, Device-Free Recognition on Your Mobile Phone: Tools, Features and a Case Study , 2013, MobiQuitous.

[19]  Jun Li,et al.  Exploiting human mobility trajectory information in indoor device-free passive tracking , 2012, IPSN.

[20]  Alessandro Epasto,et al.  Signals from the crowd: uncovering social relationships through smartphone probes , 2013, Internet Measurement Conference.

[21]  Satoshi Murata,et al.  A Human-Probe System That Considers On-body Position of a Mobile Phone with Sensors , 2013, HCI.

[22]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[23]  Ryo Kurazume,et al.  Multi-Part People Detection Using 2D Range Data , 2010, Int. J. Soc. Robotics.

[24]  Hong Wei,et al.  A survey of human motion analysis using depth imagery , 2013, Pattern Recognit. Lett..

[25]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[26]  Gwenn Englebienne,et al.  Bayesian Fusion of Ceiling Mounted Camera and Laser Range Finder on a Mobile Robot for People Detection and Localization , 2012, HBU.

[27]  Fan Yang,et al.  VM-tracking: Visual-motion sensing integration for real-time human tracking , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[28]  Chau Yuen,et al.  SenseFlow: An Experimental Study of People Tracking , 2015, RealWSN@SenSys.