Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

By Bolei Zhou, Xiaogang Wang, Xiaoou Tang. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

380 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/5a4df9bef1872865f0b619ac3aacc97f49e4a035

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Citations in the corpus

A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “7 million Commuting analytics Video surveillance Grand central station [208] 20,000 Crowd behavior COVID-19 KCDC [5], JHU [8] <100 Disease control”
Scalable Object-Oriented Sequential Generative Models (2019, methodology): “SCALOR is evaluated on the Crowded Grand Central Station dataset (Zhou et al., 2012).”
Multiple object tracking: A literature review (2014, background): “Previous studies [35], [169], [170] have been performed to analyze over-crowded scenarios such as underground train stations during peak hours and demonstration at public places.”
Category-Blind Human Action Recognition: A Practical Recognition System (2015, background): “Existing techniques for single-person action recognition are extensively surveyed in [2, 3, 8] with the majority of such methods using monocular RGB videos [9, 20, 21, 26].”
Crowded Scene Analysis: A Survey (2015, methodology): “Addressing this, some researchers have extended this kind of approaches to highly crowd scenes, by utilizing low-level features and probability models instead of tracking a single object [8], [79].”
PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction (2019, background): “A number of datasets for trajectory prediction contain videos collected from a top-down view [18, 25, 22, 31] or surveillance camera perspective [23, 4, 45].”
Human motion trajectory prediction: a survey (2019, background): “indoor (Zhou et al. 2012; Brščić et al. 2013; Rudenko et al. 2019) and outdoor environments (Pellegrini et al.”
Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction (2014, background): “It is impossible to list them all, and we only mention a few [25, 26, 28–30, 10, 11, 31, 32].”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, methodology): “The problem of crowd counting and density estimation is of paramount importance and it is essential for building higher level cognitive abilities in crowded scenarios such as crowd monitoring [15] and scene understanding [87, 115].”
SCALOR: Generative World Models with Scalable Object Representations (2019, methodology): “Speciﬁ-cally, we consider the Crowded Grand Central Station dataset (Zhou et al., 2012), which was collected from CCTV cameras at New York City’s Grand Central Station.”
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation (2015, methodology): “16, we showed an averaged stationary time distribution in four hours of the Train Station dataset [31] (see Fig.”
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction (2019, background): “As a challenging task, the human trajectory prediction has attracted considerable attention in computer vision [42, 23, 28, 21, 24, 39, 20] and robotics [5, 19] ﬁelds over the past few years.”