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survey

Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends

Published: 30 April 2019 Publication History

Abstract

In recent years, visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real-world problems such as human-computer interaction, autonomous vehicles, robotics, surveillance, and security just to name a few. In the current study, we review latest trends and advances in the tracking area and evaluate the robustness of different trackers based on the feature extraction methods. The first part of this work includes a comprehensive survey of the recently proposed trackers. We broadly categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs. Each category is further classified into various types based on the architecture and the tracking mechanism. In the second part of this work, we experimentally evaluated 24 recent trackers for robustness and compared handcrafted and deep feature based trackers. We observe that trackers using deep features performed better, though in some cases a fusion of both increased performance significantly. To overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms. We analyze the performance of trackers over 11 different challenges in OTTC and 3 other benchmarks. Our study concludes that Discriminative Correlation Filter (DCF) based trackers perform better than the others. Our study also reveals that inclusion of different types of regularizations over DCF often results in boosted tracking performance. Finally, we sum up our study by pointing out some insights and indicating future trends in the visual object tracking field.

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    cover image ACM Computing Surveys
    ACM Computing Surveys  Volume 52, Issue 2
    March 2020
    770 pages
    ISSN:0360-0300
    EISSN:1557-7341
    DOI:10.1145/3320149
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    • Sartaj Sahni
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    Publication History

    Published: 30 April 2019
    Accepted: 01 January 2019
    Revised: 01 January 2019
    Received: 01 July 2018
    Published in CSUR Volume 52, Issue 2

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    1. Robustness of tracking algorithms
    2. object tracking
    3. surveillance
    4. tracking evaluation

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