Fine-grained activity recognition by aggregating abstract object usage

By Donald J. Patterson, Dieter Fox, Henry A. Kautz, Matthai Philipose. 2005

527 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Bernt Schiele
2. Qiang Yang
3. Trevor Darrell
4. Luc Van Gool
5. Li Fei-Fei
6. Sergey Levine
7. Deva Ramanan
8. Vincent Wenchen Zheng

In Computer Vision and Pattern Recognition

1. Bernt Schiele
2. Ashutosh Saxena
3. Luc Van Gool
4. Dacheng Tao
5. Jitendra Malik
6. Xiaogang Wang
7. Antonio Torralba
8. Abhinav Kumar Gupta

In Human-Computer Interaction

1. Bernt Schiele
2. Andreas Bulling
3. Ulf Blanke
4. Jaeyong Sung
5. Qiang Yang
6. Ashutosh Saxena
7. Bart Selman
8. Deva Ramanan

Citations in the corpus

• Sensor-Based Activity Recognition (2012, unknown): “[78] performed fine-grained activity recognition (i.”
• A Survey on Ambient Intelligence in Health Care (2013, unknown): “In response, researchers have explored the usage of RFID tags [53], [54] and accelerometers or shake sensors [55] for tagging these objects and using the data for activity recognition.”
• Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach (2010, background): “…Narasimhan, and Vasudevan 2006; Park, Hong, and Cho 2007), and hot spots for tourism (Zheng et al. 2009); or only recognizing the user activities from sensor data rather than providing location and activity recommendations together (Bui 2003; Patterson et al. 2005; Zheng, Hu, and Yang 2009).”
• A tutorial on human activity recognition using body-worn inertial sensors (2014, methodology): “…temporal probabilistic models such as Hidden Markov Models (HMMs) [Rabiner 1989; Bulling et al. 2008; Fink 2008], Conditional Random Fields (CRFs) [Liao et al. 2005; van Kasteren et al. 2008; Blanke and Schiele 2010], or dynamic Bayesian networks [Patterson et al. 2005] have been used.”
• The Semantic Web (2011, unknown): “Finally, CSVW [12] also adopts this approach but targets the CSV data format.”
• Cross-domain activity recognition (2009, methodology): “Using similar approaches except the MMD function replaced with the cosine similarity function, we report our results for the Amsterdam dataset [27] and the Intel dataset [17] in Table 3.”
• Human Activity Detection from RGBD Images (2011, background): “However, this may require subjects to wear RFID sensors in an environment labeled with RFID tags, as was shown in (Philipose et al., 2004; Patterson et al., 2005), or highly engineered and task specific visual recognition procedures (Mihailidis et al., 2003).”
• Detecting activities of daily living in first-person camera views (2012, background): “There has been a fair amount of work on everyday activity-recognition from the ubiquitous computing community [31, 39, 29] , much of it addressed from a “lifelogging” perspective [2, 30] .”
• Gesture spotting with body-worn inertial sensors to detect user activities (2008, methodology): “Our method is primarily intended as part of a large activity spotting system that uses additional information such as location , modes of locomotion, e.g. sitting standing, walking [4], supplementary location sensors [5] or information on objects involved in the activity [6].”