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id :	930
type :	PhD_Thesis
dateandtime :	2020-08-11 15:00:00
duration :	120 min.
	Recommended duration for PhD thesis is 90 minutes, for other seminar types, it is 60 minutes. The duration specified here is used to reserve the room.
place :	A105
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departmental :	yes
title :	MULTILEVEL OBJECT TRACKING ON BIG GRAPH DATA USING INTERVAL TYPE-2 FUZZY SYSTEMS IN WIRELESS MULTIMEDIA SENSOR NETWORKS
author :	CIHAN KUCUKKECECI
supervisors :	PROF.DR.ADNAN YAZICI
	Supervisors field is applicable especially for a Thesis Defense
company :	Computer Engineering Dept. Middle East Technical Univ.
country :	Turkey
abstract :	Wireless multimedia sensor networks (WMSN) are the key elements of automation systems applied in different domains from monitoring of an industrial site to home security for detecting unexpected visitors, from traffic control in a metropolis to immigrant surveillance at a border station. In most of the applications, sensor data needs to be processed to reveal the information hidden as patterns or data sequences which is called data analytics. In surveillance applications, this is more crucial since tracking objects in a highly sensitive area or monitoring border for illegal activities are mission critical. However, interpretation of raw sensor data and unveiling the information inside remains a challenging issue from many aspects. As the measurement interval of the sensor data is frequent, data needs to be treated as big data because of the volume and velocity. Unfortunately, traditional approaches don’t perform well in big data analytics, especially in extracting the complex relationships between data. In this dissertation, a novel fuzzy object tracking approach which is developed using a graph-based big data model is proposed by utilization of a multilevel fusion. This approach consists of three main steps: intra-node fusion, inter-node fusion, and object trajectory construction. Intra-node fusion exploits object detection and tracking in each sensor while inter-node fusion uses spatiotemporal data along with neighbor sensors. Then, all trajectories from all sensor nodes are integrated using fuzziness to construct trajectories in the common ground-plane across the wireless multimedia sensor network. Since uncertainty naturally exists in trajectory data, type-1 and interval type-2 fuzzy logic systems have been studied on the extracted trajectories as well as for further analytics like trajectory prediction and anomaly detection. A prototype system was implemented and several experiments conducted to evaluate the performance of the proposed approach with both synthetic and real world datasets. GeoLife dataset and Maritime Cadastre dataset were utilized as input of two different real world use cases to perform experiments. The results show that usage of thirdlevel fusion, which is called object trajectory construction, in addition to inter-node and intra-node fusions provides significantly better performance for object tracking in WMSN applications. Furthermore, interval type-2 fuzzy logic utilization improves performance in both trajectory extraction and analytics
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