Pedestrian counting with grid-based binary sensors based on Monte Carlo method

Author:

Fujii Shuto,Taniguchi Yoshiaki,Hasegawa Go,Matsuoka Morito

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference27 articles.

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4. Chen Q, Gao M, Ma J, Zhang D, Ni LM, Liu Y: MOCUS: moving object counting using ultrasonic sensor networks. Int J Sensor Netw 2008, 3(1):55-65. 10.1504/IJSNET.2008.016462

5. Cui J, Zha H, Zhao H, Shibasaki R: Laser-based detection and tracking of multiple people in crowds. Comput Vis Image Understand 2007, 106: 300-312. 10.1016/j.cviu.2006.07.015

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1. Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes;Sensors;2020-02-06

2. A Dataset for Evaluating Pedestrian Event Classification Algorithms;Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015);2019

3. Online Guest Detection in a Smart Home Using Pervasive Sensors and Probabilistic Reasoning;Lecture Notes in Computer Science;2018

4. Role of Deep LSTM Neural Networks and Wi-Fi Networks in Support of Occupancy Prediction in Smart Buildings;2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS);2017-12

5. Tracking Pedestrians across Multiple Microcells Based on Successive Bayesian Estimations;The Scientific World Journal;2014

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