RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety

Wallner, Marco; Steininger, Daniel; Widhalm, Verena; Schörghuber, Matthias; Beleznai, Csaba

doi:10.1007/978-3-030-68787-8_47

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…Single object tracking algorithms became popular and gained interest in resent years because of its wide range of applications in computer vision, including video surveillance [3], augmented reality [4], automated driving [5], mobile robotics [6], traffic monitoring [7], sports video analysis [8], scene understanding [9], and human computer interaction [10]. Single object VOT approaches captures the target's appearance features in the first frame of a video sequence and then use it to locate the target in the remaining frames.…”

Section: Introductionmentioning

confidence: 99%

Transformers in Single Object Tracking: An Experimental Survey

Thangavel¹,

Thanikasalam²,

Ramanan³

et al. 2023

Preprint

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Single object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single object tracking due to their superior tracking robustness. Although several survey studies have been conducted to analyze the performance of trackers, there is a need for another survey study after the introduction of Transformers in single object tracking. In this survey, we aim to analyze the literature and performances of Transformer tracking approaches. Therefore, we conduct an in-depth literature analysis of Transformer tracking approaches and evaluate their tracking robustness and computational efficiency on challenging benchmark datasets. In addition, we have measured their performances on different tracking scenarios to find their strength and weaknesses. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they face, and their future directions.

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Section: Introductionmentioning

confidence: 99%

Transformers in Single Object Tracking: An Experimental Survey

Thangavel¹,

Thanikasalam²,

Ramanan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…mobile robotics [6], traffic monitoring [7], sports video analysis [8], scene understanding [9], and human computer interaction [10]. Single object VOT approaches captures the target's appearance features in the first frame of a video sequence and then use it to locate the target in the remaining frames.…”

Section: Introductionmentioning

confidence: 99%

Transformers in Single Object Tracking: An Experimental Survey

Kugarajeevan,

Kokul,

Ramanan

et al. 2023

IEEE Access

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Single-object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single-object tracking by introducing new perspectives and achieving superior tracking robustness. In this paper, we conduct an in-depth literature analysis of Transformer tracking approaches by categorizing them into CNN-Transformer based trackers, Two-stream Two-stage fully-Transformer based trackers, and One-stream One-stage fully-Transformer based trackers. In addition, we conduct experimental evaluations to assess their tracking robustness and computational efficiency using publicly available benchmark datasets. Furthermore, we measure their performances on different tracking scenarios to identify their strengths and weaknesses in particular situations. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they encounter, and the future directions they may take.INDEX TERMS Deep learning, tracking review, transformer tracking, vision transformer, visual object tracking.

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“…Meshroom has been used since 2014 in digital environment creation for the Visual Effects industry and now in many other industries including manufacturing, medical [10], cultural heritage [11], tourism , archaeology [12,13], biology [14], surveillance [15] and 3D printing.…”

Section: Introductionmentioning

confidence: 99%