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http://hdl.handle.net/10397/94419
Title: | Real-time crowd monitoring using seamless indoor-outdoor localization | Authors: | Kulshrestha, T Saxena, D Niyogi, R Cao, J |
Issue Date: | 1-Mar-2020 | Source: | IEEE transactions on mobile computing, 1 Mar. 2020, v. 19, no. 3, p. 664-679 | Abstract: | Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals’ wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users’ devices probe requests and locations. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3 percent. | Keywords: | Surveillance system Localization MAC Wi-fi Trajectory analysis Outlier/anomaly detection Smartphone |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on mobile computing | ISSN: | 1536-1233 | EISSN: | 1558-0660 | DOI: | 10.1109/TMC.2019.2897561 | Rights: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication T. Kulshrestha, D. Saxena, R. Niyogi and J. Cao, "Real-Time Crowd Monitoring Using Seamless Indoor-Outdoor Localization," in IEEE Transactions on Mobile Computing, vol. 19, no. 3, pp. 664-679, 1 March 2020 is available at https://dx.doi.org/10.1109/TMC.2019.2897561. |
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COMP-0375_Saxena_Real-Time_Crowd_Monitoring.pdf | Pre-Published version | 2.88 MB | Adobe PDF | View/Open |
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