Hierarchical Recognition of Human Activities Interacting with Objects
By Michael S. Ryoo, Jake K. Aggarwal. 2007
62 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Kristen Grauman
- Abhinav Kumar Gupta
- Xiaogang Wang
- Dacheng Tao
- Jitendra Malik
- Andrew Zisserman
- Li Fei-Fei
In Human-Computer Interaction
- Stefan Wermter
- German Ignacio Parisi
- Luiza Mici
- James M. Rehg
- Alireza Fathi
- Ali Farhadi
- Mohan Manubhai Trivedi
- Abhinav Kumar Gupta
Citations in the corpus
- A self-organizing neural network architecture for learning human-object interactions (2017, methodology): “Following a similar probabilistic integration approach, Ryoo and Ag-garwal [34] proposed a framework for the recognition of high-level activities.”
- Asynchronous Temporal Fields for Action Recognition (2016, background): “Understanding activities with emphasis on objects has been a particularly fruitful direction [25, 36, 9, 34, 54].”
- Learning to recognize objects in egocentric activities (2011, background): “Ryoo and Aggarwal [17] combine object recognition, motion estimation and semantic information for the recognition of human-object interactions .”
- A survey on activity recognition and behavior understanding in video surveillance (2013, background): “In [153] object context is used for recognition of human-object interaction.”
- A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance (2008, background): “Though intended for higher level analysis, learned paths are useful as feedback to the lower level functions as well [80].”