Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

By Lin Liao, Dieter Fox, Henry A. Kautz. Int. J. Robotics Res., 2007

470 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0b3c22e0aba108d47f11e8a5bc35f228f2849d95

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Citations in the corpus

Unstructured human activity detection from RGBD images (2011, methodology): “We have also made the dataset and code available open-source at: http://pr.cs.cornell.edu/humanactivities”
SoundSense: scalable sound sensing for people-centric applications on mobile phones (2009, background): “Although early work [36] mentions the use of microphone as a clue to context, most work found in the literature focus on the camera, accelerometer [6], and GPS [23] as sources of sensor data.”
Energy-accuracy trade-off for continuous mobile device location (2010, methodology): “Another possibility is a method based on Conditional Random Fields that learns the significant places visited and transportation modes used [17].”
A comparison of three methods for downscaling daily precipitation in the Punjab region (2011, background): “…applied to a variety of domains such as text processing, computer vision, image processing and bioinformatics, protein structure prediction, motion tracking, voice recognition, activity recognition, and information extraction (Kumar and Hebert, 2003; Liao et al, 2007; Vail et al., 2007).”
Conditional random fields for activity recognition (2007, methodology): “used a hierarchical conditional random field to recognition human activities from GPS traces [65, 69].”
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research (2009, methodology): “The work of [16, 17] uses GPS data with external knowledge about bus routes and bus stops to infer and predict a user’s transportation mode such as walking, driving, or taking a bus, and in [18, 19] models of a user’s activities and places from traces of GPS data and locations of restaurant, stores,”
Predicting future locations with hidden Markov models (2012, methodology): “For future work, we would also like to experiment with discriminative models, such as those referenced above, in order to address related sequence analysis methods, such as the classification of human location histories according to semantic categories [11, 10].”
SensLoc: sensing everyday places and paths using less energy (2010, methodology): “GPS traces using a hierarchical CRF model, and the model is trained by fitting parameters using a labeled trace [19].”
Tracking and Modeling of Human Activity Using Laser Rangefinders (2010, methodology): “In outdoor environments, GPS data can be used to build activity models of people [23, 24, 36].”
A survey of mobile phone sensing (2010, unknown): “More recently researchers [31] have used statistical techniques to not only infer significant places but also connect these to activities (e.g., gym, waiting for the bus) using just GPS traces.”
Human Activity Detection from RGBD Images (2011, methodology): “Also, GPS traces of a person can be utilized for activity recognition through a model based on hierarchical conditional random ﬁelds (CRF) (Liao, Fox, and Kautz, 2007).”