Open AccessMulti-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities
Author(s) -
Khalid Jamal Jadaa,
Latifah Munirah Kamarudin,
Waleed Noori Hussein,
Ammar Zakaria,
Syed Muhammad Mamduh Syed Zakaria
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1755/1/012043
Subject(s) - probabilistic logic , computer science , wireless sensor network , tracking (education) , real time computing , algorithm , automation , wireless , artificial intelligence , engineering , computer network , telecommunications , mechanical engineering , psychology , pedagogy
Wireless Sensor Network (WSNs) provides promising solutions for monitoring in several domains including industrial monitoring and control, home automation and smart cities, etc. There are numerous restrictions on the current development of target detection and tracking algorithms which makes them unsuitable or effective for indoor use. Such constraints include changes in the direction and changing target speeds, missing a target, and target dynamics. These issues come with difficulty in detection and tracking multiple targets. Moreover, the majority of the target tracking algorithms were presented on the conditions that the target is typically smooth with no unexpected changes that are difficult absolutely. Moreover, sensing coverage considers the crucial issue in a wireless sensor network. This paper implies an algorithm for detection and tracking of moving targets (intruders) for an indoor environment based on the probabilistic model utilizing WSN for safety and security. A mathematical model is presented to determine the optimum number of sensor nodes needed. The findings of the simulation showed that the MTDT algorithm provides a low missing target rate of less than 0.7 % for worst-case and can be utilized for different kinds of environment scenarios.