Deep People Counting in Extremely Dense Crowds

By Chuan Wang, Hua Zhang, Liang Yang, Si Liu, Xiaochun Cao. ACM Multimedia, 2015

384 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/8b793918bdf8e20e218d64cbe9f1657b47bb9ac0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Safety, Risk, Reliability and Quality

Citations in the corpus

A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning (2016, background): “The idea of using a one-look network counter to learn to count has recently been demonstrated on synthetic data patches [20] and by regression on subsampled crowd patches [21].”
Learning to Count with CNN Boosting (2016, methodology): “Two recent contributions [2, 15] have used deep models specifically for the application of crowd counting.”
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (2018, background): “Crowd counting is active an area of research with works tackling the three aspects of the problem: counting-by-regression [23], [17], [12], [4], [28], density map estimation [17], [7], [29], [20], [30] and localization [18], [22].”
Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs (2017, background): “Recent success of CNN-based methods in classification and recognition tasks has inspired researchers to employ them for the purpose of crowd counting and density estimation [37, 44, 36, 30].”
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (2018, methodology): “[30] trained a classic Alexnet style CNN model to predict crowd counts.”
Iterative Crowd Counting (2018, methodology): “More recent methods [18,4,20,15,13,16] used CNNs to tackle crowd counting.”
Switching Convolutional Neural Network for Crowd Counting (2017, methodology): “An Alexnet [7] style CNN model is trained by [15] to regress the crowd count.”
Scale Aggregation Network for Accurate and Efficient Crowd Counting (2018, background): “[26] modified AlexNet [27] for directly predicting the count.”
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation (2017, methodology): “An end-to-end CNN model adopted from AlexNet is constructed [36] recently for counting in extreme crowd scenes.”
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (2018, methodology): “The approaches in [12, 34] use the AlexNet network [16] to map from crowd scene patches to global number of people by changing the output of AlexNet from 1000 to 1.”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, background): “Though existing surveys analyze various methods for crowd analysis and counting, they however cover only traditional methods that use hand-crafted features and do not take into account the recent advancements driven primarily by CNN-based approaches [87, 39, 113, 11, 85, 97, 4, 98, 111, 107, 70, 88]”
Bayesian Loss for Crowd Count Estimation With Point Supervision (2019, methodology): “To avoid the more complex detection problem, some researchers proposed to directly learn a mapping from image features to their counts [5, 35, 7, 23, 46, 36, 6].”
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting (2017, background): “More recently, due to the success of CNNs in various computer vision tasks, several CNN-based approaches have been developed for crowd counting [28, 31, 15, 16].”
FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks (2017, background): “Recent years, more and more applications based on CNN models are spotlighted, such as handwriting recognition [18], face detection [9], [12], video surveillance [27], nature language analysis [10], [11], [24], and intelligent transportation [14], [28], [30].”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, methodology): “Recent advances in CNNs have been exploited for the task of crowd counting and these methods [4, 5, 6, 7, 7, 22, 23, 52, 53, 54, 55, 56] have demonstrated significant improvements over the traditional methods.”
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting (2016, background): “Wang et al. [18] also train a deep model for crowd count estimation.”
Distribution Matching for Crowd Counting (2020, methodology): “Later methods [5, 6, 49, 7] avoid the detection problem and directly learn to regress the count from a feature vector.”
Improving Object Counting with Heatmap Regulation (2018, methodology): “Considering this fact, where counting is the only task at hand, convolutional networks are employed as a high-dimensional regression network to generate a real-valued or discrete count from the input image directly [24,3,6,5,4,7].”