Tracking People by Evolving Social Groups: An Approach with Social Network Perspective

Feng, Linan; Bhanu, Bir

doi:10.1109/wacv.2015.22

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…From exo-centric perspective, common approaches include probabilistic progression based methods [13,26,71,178,257,258], graph-based approaches [24,58,115,165], clustering methods Ge et al [70], Solera et al [208], and social force model based approaches [132,154,237]. From ego-centric perspectives, grouping becomes more difficult because sensor errors and pedestrian occlusions are present.…”

Section: Formations and Social Spacesmentioning

confidence: 99%

Core Challenges of Social Robot Navigation: A Survey

Mavrogiannis¹,

Baldini²,

Wang³

et al. 2021

Preprint

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Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in public pedestrian environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in motion planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that we aspire will drive research in the field over the coming decade.

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Section: Formations and Social Spacesmentioning

confidence: 99%

Core Challenges of Social Robot Navigation: A Survey

Mavrogiannis¹,

Baldini²,

Wang³

et al. 2021

Preprint

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show abstract

“…This problem is addressed from an exo-centric perspective where the sensor is placed overhead. Many approaches include probabilistic methods such as particle filters [2,11,12,21,42,48,93,99,101,[140][141][142][143], graph-based approaches such as generalized minimum clique graphs [19,39,70,81,95,145], clustering-based methods such as k-means and agglomerative clustering [45,47,120,121], as well as methods based on the social force model [78,88,119,123].…”

Section: Group Detection and Trackingmentioning

confidence: 99%

Robot-Centric Perception of Human Groups

Taylor

Chan

Riek

2020

J. Hum.-Robot Interact.

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The robotics community continually strives to create robots that are deployable in real-world environments. Often, robots are expected to interact with human groups. To achieve this goal, we introduce a new method, the Robot-Centric Group Estimation Model (RoboGEM), which enables robots to detect groups of people. Much of the work reported in the literature focuses on dyadic interactions, leaving a gap in our understanding of how to build robots that can effectively team with larger groups of people. Moreover, many current methods rely on exocentric vision, where cameras and sensors are placed externally in the environment, rather than onboard the robot. Consequently, these methods are impractical for robots in unstructured, human-centric environments, which are novel and unpredictable. Furthermore, the majority of work on group perception is supervised, which can inhibit performance in real-world settings. RoboGEM addresses these gaps by being able to predict social groups solely from an egocentric perspective using color and depth (RGB-D) data. To achieve group predictions, RoboGEM leverages joint motion and proximity estimations. We evaluated RoboGEM against a challenging, egocentric, real-world dataset where both pedestrians and the robot are in motion simultaneously, and show RoboGEM outperformed two state-of-the-art supervised methods in detection accuracy by up to 30%, with a lower miss rate. Our work will be helpful to the robotics community, and serve as a milestone to building unsupervised systems that will enable robots to work with human groups in real-world environments.

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“…Grouping, people intend to walk together, is another kind of social interaction that occurs frequently in the tracking scenario [16]. Because this work performs tracking online, we cannot access the complete pedestrians' trajectories to derive global groups, as in offline tracking methode [7], [17]. In this work, we instead infer a simple framewise pairing grouping relation.…”

Section: Analysis For Groupingmentioning

confidence: 99%

“…Others [4] focus on solving the issue of missed targets by utilizing scene knowledge caused by occlusion. In contrast with these state-of-the-art approaches, research on social-force interactions use spatio-temporal information for computer-vision tasks such as tracking [5]- [7], activity recognition [8], [9], and abnormality detection [10]. However, most of them ignore the fact that inter-person occlusion may be caused by social interaction.…”

Section: Introductionmentioning

confidence: 99%

Inter-Person Occlusion Handling with Social Interaction for Online Multi-Pedestrian Tracking

Shen

2016

IEICE Trans. Inf. & Syst.

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SUMMARY Inter-person occlusion handling is a critical issue in the field of tracking, and it has been extensively researched. Several state-ofthe-art methods have been proposed, such as focusing on the appearance of the targets or utilizing knowledge of the scene. In contrast with the approaches proposed in the literature, we propose to address this issue using a social interaction model, which allows us to explore spatio-temporal information pertaining to the targets involved in the occlusion situation. Our experimental results show promising results compared with those obtained using other methods. key words : image processing, computer vision, inter-person occlusion, online tracking

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Tracking People by Evolving Social Groups: An Approach with Social Network Perspective

Cited by 6 publications

References 14 publications

Core Challenges of Social Robot Navigation: A Survey

Core Challenges of Social Robot Navigation: A Survey

Robot-Centric Perception of Human Groups

Inter-Person Occlusion Handling with Social Interaction for Online Multi-Pedestrian Tracking

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