Who are you with and where are you going?

Yamaguchi, Kota; Berg, Alexander C.; Ortiz, Luis E.; Berg, Tamara L.

doi:10.1109/cvpr.2011.5995468

Cited by 426 publications

(414 citation statements)

References 13 publications

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“…They detect pairwise grouping based on social behavior, which is later used to create a general grouping graph used for the tracking. Yamaguchi et al also model social factors, but take into account environmental cues as well to improve their tracking method [39]. Alahi et al propose the use of social affinity maps (SAM) to predict the destination of people in densely crowded spaces [2].…”

Section: Socially-aware Behavior Analysismentioning

confidence: 99%

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Moreno

Poppe

2016

J Multimodal User Interfaces

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Tag is a popular children's playground game. It revolves around taggers that chase and then tag runners, upon which their roles switch. There are many variations of the game that aim to keep children engaged by presenting them with challenges and different types of gameplay. We argue that the introduction of sensing and floor projection technology in the playground can aid in providing both variation and challenge. To this end, we need to understand players' behavior in the playground and steer the interactions using projections accordingly. In this paper, we first analyze the behavior of taggers and runners in a traditional tag setting. We focus on behavioral cues that differ between the two roles. Based on these, we present a probabilistic role recognition model. We then move to an interactive setting and evaluate the model on tag sessions in an interactive tag playground. Our model achieves 77.96 % accuracy, which demonstrates the feasibility of our approach. We identify several avenues for improvement. Eventually, these should lead to a more thorough understanding of what happens in the playground, not only regarding player roles but also when the play breaks down, for example when players are bored or cheat.

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Section: Socially-aware Behavior Analysismentioning

confidence: 99%

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Moreno

Poppe

2016

J Multimodal User Interfaces

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“…Optimization algorithm such as the Gradientbased Newton method and the Genetic Algorithm are used to calibrate parameters of SFM [2] and the reciprocal velocity obstacles model [33]. For example, Yamaguchi et al [32] designed an automatic method to estimate the setting of personal and social factors in their behavior model with machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The other class of the existing works focuses on designing automatic methods to calibrate model parameters [32], [33], [39]. Optimization algorithm such as the Gradientbased Newton method and the Genetic Algorithm are used to calibrate parameters of SFM [2] and the reciprocal velocity obstacles model [33].…”

Section: Related Workmentioning

confidence: 99%

A Generic Bi-Layer Data-Driven Crowd Behaviors Modeling Approach

Xing

Zhao

Zhang

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYCrowd modeling and simulation is an active research field that has drawn increasing attention from industry, academia and government recently. In this paper, we present a generic data-driven approach to generate crowd behaviors that can match the video data. The proposed approach is a bi-layer model to simulate crowd behaviors in pedestrian traffic in terms of exclusion statistics, parallel dynamics and social psychology. The bottom layer models the microscopic collision avoidance behaviors, while the top one focuses on the macroscopic pedestrian behaviors. To validate its effectiveness, the approach is applied to generate collective behaviors and re-create scenarios in the Informatics Forum, the main building of the School of Informatics at the University of Edinburgh. The simulation results demonstrate that the proposed approach is able to generate desirable crowd behaviors and offer promising prediction performance. key words: agent-based modeling, crowd simulation, data-driven approach, pedestrian behaviors

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“…In [10] and [25], the social forces are included in the motion model of the Kalman or Extended Kalman filter. In [26] a method is presented to detect small groups of people in a crowd, but it is only recently that grouping behavior has been included in a tracking framework [11,27,28]. In [28] groups are included in a graphical model which contains cycles and, therefore, Dual Decomposition [29] is needed to find the solution, which obviously is computationally much more expensive than using Linear Programming.…”

Section: Related Workmentioning

confidence: 99%

“…Though each object can be tracked separately, recent works have proven that tracking objects jointly and taking into consideration their interaction can give much better results in complex scenes. Current research is mainly focused on two aspects to exploit the interaction between pedestrians: the use of a global optimization strategy [8,9] and a social motion model [10,11]. The focus of this paper is to marry the concepts of global optimization and social and grouping behavior to obtain a robust tracker able to work in crowded scenarios.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Pedestrian Interaction via Global Optimization and Social Behaviors

Leal-Taixé

Pons-Moll

Rosenhahn

2012

Lecture Notes in Computer Science

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Abstract. Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.

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Who are you with and where are you going?

Cited by 426 publications

References 13 publications

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

A Generic Bi-Layer Data-Driven Crowd Behaviors Modeling Approach

Exploiting Pedestrian Interaction via Global Optimization and Social Behaviors

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