Spatiotemporal Group Context for Pedestrian Counting
By Jinqiao Wang, Wei Fu, Jingjing Liu, Hanqing Lu. 2014
25 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition
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
- Hanqing Lu
- Dimitris N. Metaxas
- Zhenwei Shi
- Shaoting Zhang
- Jingjing Liu
- Shu Wang
- Xu Zhao
- Chaoyang Zhao
In Biomedical Engineering
In Computer Vision and Pattern Recognition
- Zhenwei Shi
- Hanqing Lu
- Dimitris N. Metaxas
- Shaoting Zhang
- Jinqiao Wang
- Xu Zhao
- Dacheng Tao
- Jingjing Liu
Citations in the corpus
- CoupleNet: Coupling Global Structure with Local Parts for Object Detection (2017, background): “In addition, context [25] is known to play an important role in visual recognition.”
- Multispectral Deep Neural Networks for Pedestrian Detection (2016, background): “As a canonical case of general object detection problem, in the past decades, pedestrian detection has attracted consistent attention from computer vision community [2, 4, 5, 9, 23, 24, 25, 26, 29, 43, 45].”
- A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection (2020, background): “Pyramid spatial–temporal attention module: Context plays an important role in many visual tasks, such as video surveillance [53], semantic segmentation [46,54] and object detection [55].”