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Visual Object Tracking Using Machine Learning

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Science, Engineering Management and Information Technology (SEMIT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1809))

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Abstract

Visual object tracking has become a very active research area in recent years. Each year, a growing number of tracking algorithms are proposed. Object detection and tracking is a critical and challenging task in many critical computer vision applications, including automated video surveillance, traffic monitoring, autonomous robot navigation, and intelligent environments. Object tracking is segmenting an object of interest and tracking its velocity, orientation, and occlusion in a video scene to extract useful information. Over the last two decades, several object tracking approaches have been developed to design a robust object tracker that covers all practical obstacles in real-world operations. This paper reviews recent trends and advances in tracking and assesses the reliability of various trackers based on feature extraction techniques. In video processing, visual tracking has a wide range of applications. When a target is identified in one video frame, it is frequently advantageous to track that object in subsequent frames. Every successful frame in which the target is tracked yields more information about the target’s identity and activity. Because tracking is more straightforward than detection, tracking algorithms can require fewer computational resources than object detectors.

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Acknowledgments

This research was supported by Princess Sumaya University for Technology (PSUT) and Researchers Supporting Program (TUMA-Project-2021-14), AlMaarefa University.

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Authors and Affiliations

  1. Department of Computer Science, King Hussein School of Computing Sciences, Princess Sumaya University for Technology Amman, Amman, Jordan

    Ammar Odeh & Mustafa Al-Fayoumi

  2. Department of Computer Science, College of Applied Sciences, AlMaarefa University Riyadh, Riyadh, Kingdom of Saudi Arabia

    Ismail Keshta

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  1. Ammar Odeh
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  2. Ismail Keshta
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  3. Mustafa Al-Fayoumi
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Correspondence to Ammar Odeh .

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Editors and Affiliations

  1. Kharazmi University, Tehran, Iran

    Abolfazl Mirzazadeh

  2. Ankara Yıldırım Beyazıt University, Ankara, Türkiye

    Babek Erdebilli

  3. Istinye University, Istanbul, Türkiye

    Erfan Babaee Tirkolaee

  4. Poznań University of Technology, Poznań, Poland

    Gerhard-Wilhelm Weber

  5. Indian Institute of Technology Delhi, New Delhi, India

    Arpan Kumar Kar

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Odeh, A., Keshta, I., Al-Fayoumi, M. (2023). Visual Object Tracking Using Machine Learning. In: Mirzazadeh, A., Erdebilli, B., Babaee Tirkolaee, E., Weber, GW., Kar, A.K. (eds) Science, Engineering Management and Information Technology. SEMIT 2022. Communications in Computer and Information Science, vol 1809. Springer, Cham. https://doi.org/10.1007/978-3-031-40398-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-40398-9_4

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