Learning activity patterns using fuzzy self-organizing neural network

By Weiming Hu, Dan Xie, Tien-Ping Tan, S. Maybank. 2004

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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

1. German Ignacio Parisi
2. Stefan Wermter
3. Luiza Mici
4. Ramalingam Chellappa
5. Chee Seng Chan
6. Chern Hong Lim
7. Ekta Vats
8. Tien-Ping Tan

In Computer Vision and Pattern Recognition

1. German Ignacio Parisi
2. Stefan Wermter
3. Tien-Ping Tan
4. Luiza Mici
5. Ramalingam Chellappa
6. Luc Van Gool
7. Weiming Hu
8. Dacheng Tao

Citations in the corpus

• An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “[24], who modeled time explicitly by adding lateral connections between neurons in the state layer, obtaining performances comparable to that of the probabilistic models.”
• Machine Recognition of Human Activities: A Survey (2008, background): “A related application involves searching for an activity of interest in a large database by learning patterns of activity from long videos [14], [15].”
• A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance (2008, unknown): “ples [27].”
• A survey on activity recognition and behavior understanding in video surveillance (2013, background): “A related application involves searching for an activity of interest in a large database by learning patterns of activity from long videos [60, 180].”
• Fuzzy human motion analysis: A review (2014, methodology): “ component analysis were employed to learn the multi-prototype action classes in the multiple camera environment. Fuzzy clustering Difficulty in determining the optimum parameters in the fuzzy system. [172, 173, 174] Integration of the fuzzy approaches with machine learning algorithms allows the lear”
• Generalizing self-organizing map for categorical data (2006, background): “For the encoding of an animal (for example, a hen), the three attributes Size_small, 2_legs, and Has_feathers are set to 1, and the other attributes are set to 0.”
• Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach (2011, unknown): “drastically improved the learning process speed [13].”
• Similarity based vehicle trajectory clustering and anomaly detection (2005, methodology): “In our previous work [4], fuzzy versions of self-organizing maps were investigated for learning motion patterns from trajectories.”