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Supervised Machine Learning Approaches for Moving Object Tracking: A Survey

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Abstract

Object tracking is a very interesting problem in computer vision. Numerous algorithms have been developed to solve object tracking problems for several decades. Among various techniques, in this article, we review most of the existing traditional supervised machine learning-based moving object tracking approaches before the year 2017. We also discuss the several evaluation measures and various datasets considered in the literature. We hope that this survey helps the readers to acquire valuable knowledge about the literature of traditional supervised learning-based tracking algorithms and to choose the most suitable algorithm for their particular tracking tasks.

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Notes

  1. Object, target and object of interest are used inter changeably in this article.

  2. Or test frame or current frame or \(t\text {th}\) frame.

  3. Or training frame or previous frame or \((t-1){\text {th}}\) frame.

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    Ajoy Mondal

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Mondal, A. Supervised Machine Learning Approaches for Moving Object Tracking: A Survey. SN COMPUT. SCI. 3, 146 (2022). https://doi.org/10.1007/s42979-022-01040-0

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