Single Object Tracking Using Estimation Algorithms

Seth, R. Koslov; Swain, Subrat Kumar; Mishra, Sudhanshu

doi:10.1109/epetsg.2018.8658715

Cited by 3 publications

(2 citation statements)

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“…Kalman filter and exponential smoothing are often used to filter noise data from data iteration. Seth, Swain & Mishra (2018) used the traditional Kalman filter to estimate the position and trajectory of a single target in motion, and obtained the actual trajectory by connecting the center of the obtained moving object image. For the processing of vehicle-related data in the IoV, Zhang et al (2018) used Kalman filter for the selection of data from simple inertial navigation and data from various positioning system sources with different errors, which can effectively improve accuracy and reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since federated learning is an emerging field, its use in handling noise data is rarely covered. So this article refers to Ahmed et al (2020) , Ye et al (2020) , Li, Wang & Guan (2019) , Xu et al (2022) , Seth, Swain & Mishra (2018) , Zhang et al (2018) for a comparative analysis of federated learning algorithms applied to different domains with the mechanism proposed in this article, as shown in Table 1 .…”

Section: Literature Reviewmentioning

confidence: 99%

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OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei

Wang

Caiyu

et al. 2022

PeerJ Computer Science

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The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.

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Section: Literature Reviewmentioning

confidence: 99%