Egocentric Meets Top-View

Ardeshir, Shervin; Borji, Ali

doi:10.1109/tpami.2018.2832121

Cited by 24 publications

(13 citation statements)

References 40 publications

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“…In addition to this perception, they looked at what type of camera position (top view or first-person view) could be better for the perception of density. The work of Ardeshir and Borji [18] shows experiments and graphs made between two points of view (first-person and top cam view), thus helping in the integration and use of the types of cameras used in this present work.…”

Section: Related Workmentioning

confidence: 98%

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Araujo

Favaretto

Knob

et al. 2019

Preprint

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This work aims to evaluate people's perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. The goal is to focus on their behavior and not being distracted by other features. In addition to tracking files containing their positions, the dataset also contains pedestrian emotions and personalities detected using Computer Vision and Pattern Recognition techniques. We proceed with our analysis in order to answer the question if subjects can perceive geometric features as distances/speeds as well as emotions and personalities in video sequences when pedestrians are represented by virtual humans. Regarding the participants, an amount of 73 people volunteered for the experiment. The analysis was divided in two parts: i) evaluation on perception of geometric characteristics, such as density, angular variation, distances and speeds, and ii) evaluation on personality and emotion perceptions. Results indicate that, even without explaining to the participants the concepts of each personality or emotion and how they were calculated (considering geometric characteristics), in most of the cases, participants perceived the personality and emotion expressed by the virtual agents, in accordance with the available ground truth.

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Section: Related Workmentioning

confidence: 98%

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Araujo

Favaretto

Knob

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The egocentric (first-person) view and the exocentric (thirdperson) view are drastically different in nature and relating them has been a challenging problem in computer vision. The relationship has been explored in many tasks such as human identification [20], [21], semantic segmentation [22], action recognition [18], [19] and cross-view image synthesis [17], [23]. However, these prior works required paired datasets of first/third person views that are simultaneously recorded to train CNNs.…”

Section: B Joint Understanding First/third Person View Domainmentioning

confidence: 99%

Camera Selection for Occlusion-Less Surgery Recording via Training With an Egocentric Camera

et al. 2021

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Recording surgery is an important technique for education and the evaluation of medical treatments. However, capturing targets such as the surgical field, surgical tools, and the surgeon's hands, is almost impossible since these targets are heavily occluded by the surgeon's head and body during a surgery. We used a recording system in which multiple cameras are installed in the surgical lump, supposing at least one camera would capture the target without occlusion. As this system records multiple video sequences, we address the task to select a best view camera automatically. Recently, learning-based approaches in a fully supervised manner have been proposed for this task, but these previous approaches completely rely on manual annotation of the training data. In this paper, we focus on the eye tracker mounted on the surgeon's head, which can capture the recording targets without occlusion. Employing this first-person-view video synchronized with multiple videos of the surgical lump, we propose a novel camera selection approach using a self-supervised learning framework. In experiments, we created a dataset composed of four different breast surgery. Our extended experiments showed that our approach successfully switched to the best camera view without manual annotation and achieved competitive accuracy compared with conventional supervised methods. Also, our approach yielded effective visual representations comparable to state-of-the-art selfsupervised learning frameworks.

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“…Top view and horizontal view are two complementary views and the conjoint analysis of them have drawn much attention recently. Ardeshir and Borji (2016;2018a) propose a method to identify camera wearers on a top-view video given the horizontalview videos recorded by these wearable cameras. Similarly, given a horizontal video and a top-view video, Ardeshir and Borji (2018b) study how to identify the horizontalview camera holder in the top-view video, and re-identify the subjects present in both the horizontal-and top-view videos.…”

Section: Related Workmentioning

confidence: 99%

Complementary-View Multiple Human Tracking

Han

Feng

Zhao

et al. 2020

AAAI

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The global trajectories of targets on ground can be well captured from a top view in a high altitude, e.g., by a drone-mounted camera, while their local detailed appearances can be better recorded from horizontal views, e.g., by a helmet camera worn by a person. This paper studies a new problem of multiple human tracking from a pair of top- and horizontal-view videos taken at the same time. Our goal is to track the humans in both views and identify the same person across the two complementary views frame by frame, which is very challenging due to very large field of view difference. In this paper, we model the data similarity in each view using appearance and motion reasoning and across views using appearance and spatial reasoning. Combing them, we formulate the proposed multiple human tracking as a joint optimization problem, which can be solved by constrained integer programming. We collect a new dataset consisting of top- and horizontal-view video pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

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Egocentric Meets Top-View

Cited by 24 publications

References 40 publications

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Camera Selection for Occlusion-Less Surgery Recording via Training With an Egocentric Camera

Complementary-View Multiple Human Tracking

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