Crowd Analysis Using Computer Vision Techniques
By Julio C. S. Jacques Junior, Soraia Raupp Musse, Cláudio Rosito Jung. IEEE Signal Processing Magazine, 2010
415 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ce8284e2a0232ff357a2a2a2a6adc7ec91782e3f
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
- Vishal M. Patel
- Vishwanath A. Sindagi
- Sergey I. Nikolenko
- Qi Wang
- Junyu Gao
- Wei Lin
- Yuan Yuan
- Bingbing Ni
In Computer Vision and Pattern Recognition
- Vishal M. Patel
- Vishwanath A. Sindagi
- Sergey I. Nikolenko
- Shuicheng Yan
- Dacheng Tao
- Yi Yang
- Ming-Hsuan Yang
- Bingbing Ni
Citations in the corpus
- Learning From Synthetic Data for Crowd Counting in the Wild (2019, background): “Crowd counting is a branch of crowd analysis [17, 29, 18, 37], which is essential to video surveillance, public areas planning, traffic flow monitoring and so on.”
- Crowded Scene Analysis: A Survey (2015, methodology): “Some recent works have been proposed to synthesize crowd videos with realistic microscale behavior [28].”
- Self-driving cars and data collection: Privacy perceptions of networked autonomous vehicles (2017, background): “Publication of the collected data involves separate regulation and public perception, though cases generally involve simple uses such as determining the cause of an accident [21].”
- A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, background): “Researchers have attempted to provide a comprehensive survey and evaluation of existing techniques for various aspects of crowd analysis [105, 30, 44, 55, 117].”
- A Review on Video-Based Human Activity Recognition (2013, background): “[119] reviewed crowd analysis literature and tackled three important issues including people counting, people tracking, and crowd behavior understanding.”
- Synthetic Data for Deep Learning (2019, methodology): “This ties into crowd analysis, where synthetic data is used to model crowds and train visual crowd analysis tools on rendered images [549].”
- Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (2018, background): “With the rapid increase of population of major cities, crowd scene analysis [11, 33] has already become an important security technique in video surveillance [16, 20, 36].”