Segmentation and Tracking of Multiple Humans in Crowded Environments

By Tao Zhao, Ramkant Nevatia, Bo Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008

417 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9e147ee02392169f5716d0774f02b7df27f8ce1c

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In Computer Vision and Pattern Recognition

Citations in the corpus

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (2016, background): “In detectionbased crowd counting methods, people typically assume a crowd is composed of individual entities which can be detected by some given detectors [13, 34, 18, 10].”
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation (2017, methodology): “The detection based methods [8, 18, 34, 10, 43, 13] employ object detectors to loFigure 1.”
Counting People With Low-Level Features and Bayesian Regression (2012, background): “Pedestrian detection algorithms can be based on boosting appearance and motion features [1], Bayesian model-based segmentation [2], [3], histogram-of-gradients [25], or integrated top-down and bottom-up processing [4].”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, methodology): “In another approach using shape learning, Zhao et al. [112] modelled humans using 3D shapes composed of ellipsoids, and employed a stochastic process to estimate the number and shape conﬁgura-tion that best explains a given foreground mask in a scene.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “The model-based methods detect pedestrians by searching the region that matches the pedestrian shape model [166]– [168].”
Learning Multi-Level Density Maps for Crowd Counting (2020, methodology): “These methods [28], [29] succeed in low-density crowd scenes where people seldom occlude each other.”
Counting Vehicles with Cameras (2018, methodology): “Finally, in the shape-matching detection, the classifier is about object shapes, for example composed of ellipses [15].”
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments (2010, background): “Recently, some authors have suggested the use of Markov chain Monte Carlo (MCMC) sampling to find the (approximate) optimum in the joint tracking space (Khan et al. 2005; Yu et al. 2007; Zhao et al. 2008).”
Feature Mining for Localised Crowd Counting (2012, methodology): “In counting by detection [8, 16, 24], people count is estimated through detecting instances of people.”
Human detection from images and videos: A survey (2016, methodology): “[184] formulated the inference process as an optimisation problem and Markov chain Monte Carlo (MCMC) was applied to find the”