Interactive and adaptive data-driven crowd simulation

Kim, Sujeong; Bera, Aniket; Best, Andrew; Chabra, Rohan; Manocha, Dinesh

doi:10.1109/vr.2016.7504685

Cited by 42 publications

(29 citation statements)

References 33 publications

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“…Second, complementing existing rule-based models with parameters derived from videos. For the former, [7][8][9][10][11] are of particular interest in our context. The latter was carried out in [12] together with the Social Force Model.…”

Section: Introductionmentioning

confidence: 99%

Can we learn where people go?

Gödel¹,

Köster²,

Lehmberg³

et al. 2020

Coll Dyn

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In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Our research question is: Can we obtain this information from video data using machine learning methods? We use density heatmaps, which indicate the pedestrian density within a given camera cutout, as input to predict the destination distributions. For our proof of concept, we train a Random Forest predictor on an exemplary data set generated with the Vadere microscopic simulator. The scenario is a crossroad where pedestrians can head left, straight or right. In addition, we gain first insights on suitable placement of the camera. The results motivate an in-depth analysis of the methodology.

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Section: Introductionmentioning

confidence: 99%

Can we learn where people go?

Gödel¹,

Köster²,

Lehmberg³

et al. 2020

Coll Dyn

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show abstract

“…For PPNP, three times of Dijkstra's algorithm on the v-graph is involved to find c α ϵ · L L ) time to traverse those states to find the approximate solution. 22 To sum up, the time complexity of DEWN is O(N 2 + N 3 ϵ ) since |Γ v |, |E v |, and |X | are all smaller than N . Therefore, DEWN is an FPTAS of DROP.…”

Section: Algorithm 6 Round-and-scaled Dewn Dpmentioning

confidence: 99%

“…This technological trend of VR not only attracts business interests from prominent vendors such as Facebook and Alibaba 1 but also brings a new wave of research in the academia. While current research on VR mostly originated from Computer Graphics, Multimedia, and HCI, focusing on constructing vivid VR worlds [21,22,41], the needs for research and support from the database community are also imminent.…”

Section: Introductionmentioning

confidence: 99%

On VR Spatial Query for Dual Entangled Worlds

Lin

Lai

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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With the rapid advent of Virtual Reality (VR) technology and virtual tour applications, there is a research need on spatial queries tailored for simultaneous movements in both the physical and virtual worlds. Traditional spatial queries, designed mainly for one world, do not consider the entangled dual worlds in VR. In this paper, we first investigate the fundamental shortest-path query in VR as the building block for spatial queries, aiming to avoid hitting boundaries and obstacles in the physical environment by leveraging Redirected Walking (RW) in Computer Graphics. Specifically, we first formulate Dual-world Redirected-walking Obstacle-free Path (DROP) to find the minimum-distance path in the virtual world, which is constrained by the RW cost in the physical world to ensure immersive experience in VR. We prove DROP is NP-hard and design a fully polynomial-time approximation scheme, Dual Entangled World Navigation (DEWN), by finding Minimum Immersion Loss Range (MIL Range). Afterward, we show that the existing spatial query algorithms and index structures can leverage DEWN as a building block to support kNN and range queries in the dual worlds of VR. Experimental results and a user study with implementation in HTC VIVE manifest that DEWN outperforms the baselines with smoother RW operations in various VR scenarios.

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“…To model these myriad behavioral aspects, methods such as field and flow based [4], [5], force-based [6], [7], velocity and geometric optimization [8], [9], [10] and data-driven [11], [12], [13] have been proposed. Our aim is to provide an evaluation framework that imposes no assumptions on the underlying simulation mechanism and can therefore work on the output data of all such methods.…”

Section: Related Workmentioning

confidence: 99%

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Wang

Ondřej

O’Sullivan

2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.

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Interactive and adaptive data-driven crowd simulation

Cited by 42 publications

References 33 publications

Can we learn where people go?

Can we learn where people go?

On VR Spatial Query for Dual Entangled Worlds

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

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