A fast and scalable crowd sensing based trajectory tracking system

Niyogi, Rajdeep; Kulshrestha, Tarun; Patel, Dhaval

doi:10.1109/ic3.2017.8284303

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This corresponds to the last row of the table. Data transfer over WebSocket [39] functionality was also implemented in the application. However, this was changed later to internal storage during the experiments because the WebSocket connection required constant internet access.…”

Section: V2p Communication Experimentsmentioning

confidence: 99%

Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication

Gelbal,

Aksun-Guvenc,

Guvenc

2024

Electronics

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Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception sensors like cameras to detect risk and issue collision warnings or apply emergency braking. Perception sensors like cameras are highly affected by lighting and weather conditions. Cameras, radar, and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs on trajectories with a potential collision risk. An Android smartphone app with low-energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data are smoothed using a Kalman filter, and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU, and issues driver warnings with different severity levels. The warning severity level is based on the analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision-mitigation system.

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Section: V2p Communication Experimentsmentioning

confidence: 99%

Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication

Gelbal,

Aksun-Guvenc,

Guvenc

2024

Electronics

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“…We further broaden our analysis and use an Androidbased APlogger application [52] for finding the mobility pattern of individuals in the IITR campus. APlogger installed on Smartphones maintains the traces of APs available in the surrounding region with the current GPS location of Smartphone and the timestamp at which APs are detected.…”

Section: Information Retrieval From Apsmentioning

confidence: 99%

Real-Time Crowd Monitoring Using Seamless Indoor-Outdoor Localization

Kulshrestha

Saxena

Niyogi

et al. 2020

IEEE Trans. on Mobile Comput.

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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 without users' active cooperation. 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%.

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