QueueSense: Collaborative recognition of queuing on mobile phones

Nowadays people spend a substantial amount of time waiting in different places such as supermarkets and amusement parks. Detecting the status of queuing may benefit both users and business. In this paper, we present QueueSense, a queuing recognition system on mobile phones to assist in a queue management system. QueueSense extracts features of queuing behavior and classifies queueing via collaboration among people waiting in line. It measures the disparity of people in different lines using relative position changing rate and partitions different queues using a hierarchical clustering approach. We implement a prototype of QueueSense on Android platforms using widely available multi-modal sensors and it is the first queue detection system on mobile phones. We conduct real-world experiments at a dining hall and a supermarket near a university campus. Through implementation and evaluation, we demonstrate that QueueSense is capable of detecting waiting lines that occur in our daily lives with high accuracy.

[1]  R. Moe-Nilssen,et al.  Test-retest reliability of trunk accelerometric gait analysis. , 2004, Gait & posture.

[2]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[3]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[4]  Wei Pan,et al.  SoundSense: scalable sound sensing for people-centric applications on mobile phones , 2009, MobiSys '09.

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

[6]  Kun Li,et al.  MAQS: a mobile sensing system for indoor air quality , 2011, UbiComp '11.

[7]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[8]  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.

[9]  Zhigang Liu,et al.  Darwin phones: the evolution of sensing and inference on mobile phones , 2010, MobiSys '10.

[10]  Guobin Shen,et al.  BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices , 2007, SenSys '07.

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

[12]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[14]  K. Lewin,et al.  Field Theory in Social Science: Selected Theoretical Papers , 1951 .

[15]  P. Barralon,et al.  Walk Detection With a Kinematic Sensor: Frequency and Wavelet Comparison , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Romit Roy Choudhury,et al.  MoVi: mobile phone based video highlights via collaborative sensing , 2010, MobiSys '10.

[17]  Inseok Hwang,et al.  SocioPhone: everyday face-to-face interaction monitoring platform using multi-phone sensor fusion , 2013, MobiSys '13.

[18]  Lin Zhong,et al.  Self-constructive high-rate system energy modeling for battery-powered mobile systems , 2011, MobiSys '11.

[19]  Roger Bennett,et al.  Queues, customer characteristics and policies for managing waiting‐lines in supermarkets , 1998 .

[20]  M. Smith Field Theory in Social Science: Selected Theoretical Papers. , 1951 .

[21]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.