Estimation of number of people in crowded scenes using perspective transformation

By Sheng-Fuu Lin, Jaw-Yeh Chen, Hung-Xin Chao. IEEE Trans. Syst. Man Cybern. Part A, 2001

354 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/008540fd3114ec8f3e55c45c90265061b51046ef

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In Computer Vision and Pattern Recognition

Citations in the corpus

Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection (2008, methodology): “In [4], Haar features [6] are extracted and applied for head detection (shoulder is not included).”
Counting People With Low-Level Features and Bayesian Regression (2012, background): “This problem has been addressed to some extent by the development of part-based detectors [5], [6], [26], [27].”
Bayesian Loss for Crowd Count Estimation With Point Supervision (2019, background): “rlaps and occlusions between each other; 2) perspective effects may cause large variations in human size, shape, and appearance in the image. In the past decade, a number of crowd counting algorithms [22,58,21,12,5,35,20,11,7,31] have been proposed in the literature. Recently, crowd counting methods”
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms (2010, background): “Some techniques try to mitigate this issue by detecting only heads [120] or omegashaped regions formed by heads and shoulders [121].”
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation (2017, background): “Therefore, part and shape based models are introduced in [10, 20, 39], where ensembles of classifiers are built for specific body parts and regions.”
Privacy preserving crowd monitoring: Counting people without people models or tracking (2008, background): “This is a canonical example of a problem that vision technology addresses with privacy invasive methods: detect the people in the scene [3, 4, 5, 6, 7], track them over time [8, 9, 10], and count the number of tracks.”
Iterative Crowd Counting (2018, methodology): “The key highlights of our work are: crowd counting as an object detection problem [11,12].”
Crowd Analysis Using Computer Vision Techniques (2010, methodology): “Lin et al. [30] proposed an algorithm to estimate the crowd density in three stages.”
Crowd Counting Using Multiple Local Features (2009, neutral): “been proposed to estimate crowd sizes [10].”
Crowd analysis: a survey (2008, background): “[60] present a system that estimates the crowd size through the recognition of the head”
Distribution Matching for Crowd Counting (2020, methodology): “For images of large crowds, this density map estimation approach has been shown to be more robust than the detection-then-counting approach [22, 19, 62, 12] because the former is less sensitive to occlusion and it does not need to commit to binarized decisions at an early stage.”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, background): “Researchers have attempted to address this issue by adopting part-based detection methods [29, 57, 101], where one constructs boosted classiﬁers for speciﬁc body parts such as the head and shoulder to estimate the people counts in a designated area [54].”