A Review of Physics-based Methods for Group and Crowd Analysis in Computer Vision

Jo, Hyunjoon; Chug, Kabir; Sethi, Ricky J.

doi:10.14304/surya.jpr.v1n1.6

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…Surveillance, entertainment and social sciences are examples of fields that can benefit from the development of this area of study. Literature presents different applications of crowd analysis, like counting people in crowds [9,6], group and crowd movement and formation [41,45,38,26] and detection of social groups in crowds [40,39,21,10]. Normally, these approaches are based on personal tracking or optical flow algorithms, and handle with features like walking speed, directions and distances over time.…”

Section: Introductionmentioning

confidence: 99%

Detecting personality and emotion traits in crowds from video sequences

Favaretto

Knob

Musse

et al. 2018

Machine Vision and Applications

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This paper presents a methodology to detect personality and basic emotion characteristics of crowds in video sequences. Firstly, individuals are detected and tracked, then groups are recognized and characterized. Such information is then mapped to OCEAN dimensions, used to find out personality and emotion in videos, based on OCC emotion models. Although it is a clear challenge to validate our results with real life experiments, we evaluate our method with the available literature information regarding OCEAN values of different Countries and also emergent Personal distance among people. Hence, such analysis refer to cultural differences of each country too. Our results indicate that this model generates coherent information when compared to data provided in available literature, as shown in qualitative and quantitative results. Keywords Computer vision • crowd features • Big-five model • cultural dimensions • crowd emotion Thanks to Office of Naval Research Global (USA) and Brazilian agencies: CAPES, CNPQ and FAPERGS.

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Section: Introductionmentioning

confidence: 99%

Detecting personality and emotion traits in crowds from video sequences

Favaretto

Knob

Musse

et al. 2018

Machine Vision and Applications

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“…The main difference between the two is that in the former, the interaction forces between individuals in the crowd tend to dominate the motion of the individuals, while in the latter, the interactions between individuals are few and random motion is most likely to dominate the crowd behavior. In a more recent review, Jo et al [23] briefly highlighted the difference between physics-based and physics-inspired methods. Accordingly, physics-based methods are rooted in fundamental physic ideas whereas the latter are inspired by the laws of physics.…”

Section: Comparisons With Previous Reviewsmentioning

confidence: 99%

Crowd behavior analysis: A review where physics meets biology

2016

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Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irrevocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision studies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.

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“…Surveillance, entertainment, and social sciences are fields that can benefit from the development of this area of study. Literature dealt with different applications of crowd analysis, for example, counting people in crowds, group and crowd movement and formation, and detection of social groups in crowds . Normally, these approaches are based on personal tracking or optical flow algorithms and handle as features speed, directions, and distances over time.…”

Section: Introductionmentioning

confidence: 99%

Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

Favaretto

Santos²,

Balotin³

et al. 2019

Computer Animation & Virtual

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This paper presents a study, organized in two phases, regarding group behavior in a controlled experiment focused on differences in an important attribute that vary across cultures—personal spaces. First, we want to study and compare the spatial behavior different populations adopt with respect to their personal space. Second, we want to use simulation of virtual agents to artificially generate movements of people in similar situations and validate them using real video sequences. Our main goal is to be able to extract from video sequences and then simulate variations in populations in a coherent way with literature that studies cultural aspects. In addition to the cultural aspects, we also investigate the personality model in the studied videos using OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Finally, we propose a way to simulate the fundamental diagram experiment from other countries using the OCEAN psychological trait model as input. Results indicate that the simulated countries have consistent characteristics with the expected literature.

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A Review of Physics-based Methods for Group and Crowd Analysis in Computer Vision

Cited by 13 publications

References 31 publications

Detecting personality and emotion traits in crowds from video sequences

Detecting personality and emotion traits in crowds from video sequences

Crowd behavior analysis: A review where physics meets biology

Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

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