A Review on Classifications of Tracking Systems in Augmented Reality

Bhakar, Suman; Bhatt, Devershi Pallavi; Dhaka, Vijaypal Singh; Sarma, Yogender Kumar

doi:10.1088/1742-6596/2161/1/012077

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…A limitation of our study is that it used simulated AVs; in future traffic, actual AVs would need to communicate their stopping intentions wirelessly to the pedestrian’s AR headset. Furthermore, instead of outside-in tracking using base stations that emit infrared light, there would likely need to be a form of inside-out tracking, where the pedestrian’s headset detects objects in the environment (e.g., Bhakar et al, 2022 ). Although the Varjo XR-3 is a state-of-the-art AR device, there were some technical hiccups, such as a small number of occasions of loss of tracking, which caused VR objects to display an abrupt rotation, leading to some confusion or disorientation.…”

Section: Discussionmentioning

confidence: 99%

Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study

Aleva,

Tabone,

Dodou

et al. 2024

Front. Robot. AI

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Introduction: Communication from automated vehicles (AVs) to pedestrians using augmented reality (AR) could positively contribute to traffic safety. However, previous AR research for pedestrians was mainly conducted through online questionnaires or experiments in virtual environments instead of real ones.Methods: In this study, 28 participants conducted trials outdoors with an approaching AV and were supported by four different AR interfaces. The AR experience was created by having participants wear a Varjo XR-3 headset with see-through functionality, with the AV and AR elements virtually overlaid onto the real environment. The AR interfaces were vehicle-locked (Planes on vehicle), world-locked (Fixed pedestrian lights, Virtual fence), or head-locked (Pedestrian lights HUD). Participants had to hold down a button when they felt it was safe to cross, and their opinions were obtained through rating scales, interviews, and a questionnaire.Results: The results showed that participants had a subjective preference for AR interfaces over no AR interface. Furthermore, the Pedestrian lights HUD was more effective than no AR interface in a statistically significant manner, as it led to participants more frequently keeping the button pressed. The Fixed pedestrian lights scored lower than the other interfaces, presumably due to low saliency and the fact that participants had to visually identify both this AR interface and the AV.Discussion: In conclusion, while users favour AR in AV-pedestrian interactions over no AR, its effectiveness depends on design factors like location, visibility, and visual attention demands. In conclusion, this work provides important insights into the use of AR outdoors. The findings illustrate that, in these circumstances, a clear and easily interpretable AR interface is of key importance.

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Section: Discussionmentioning

confidence: 99%

Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study

Aleva,

Tabone,

Dodou

et al. 2024

Front. Robot. AI

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“…Different from these trackers, as illustrated in Fig. 8, Cho et al [51] extracted the multi-level convolutional features of the target template and search region from the last three layers (3,4,5) of the AlexNet [20] model. Their tracker is referred as HiFT and it was originally proposed for aerial tracking.…”

Section: Transformers In Vot Cnn-transformer Based Trackersmentioning

confidence: 99%

“…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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“…Single object tracking algorithms became popular and gained interest in recent years because of its wide range of applications in computer vision, including video surveillance [3], augmented reality [4], automated driving [5],…”

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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A Review on Classifications of Tracking Systems in Augmented Reality

Cited by 5 publications

References 19 publications

Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study

Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study

Transformers in Single Object Tracking: An Experimental Survey

Transformers in Single Object Tracking: An Experimental Survey

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