This video shows the experimental results from a radio tomography (RT)-based two-person tracking experiment in an apartment. The people are not wearing any radio tags, instead, a wireless network of 33 (IEEE 802.15.4) transceivers deployed in the apartment measure changes in received signal strength (RSS), known as "the number of bars", caused by the people. Based on which links experience changes, the RT algorithm comes up with an image (shown at left) that has highest values (red) where it guesses that a person is located, and lowest values (blue) where it guesses that no person is located. A multi-target tracking algorithm developed by Dr. Maurizio Bocca at the University of Utah identifies from the image where the "blobs" are and what path they are taking through the apartment, using computer vision methods adapted to the RT problem. On the left, the video shows the apartment with black indicating walls, grey indicating furniture, white circle indicating the actual person location, and white X indicating the current estimate of the person. Dr. Bocca's algorithm track the two people to within an average error of about 30 cm (1 foot). Dr. Bocca and his University of Utah team used this system to compete in the EvAAL 2012 tracking competition and win 1st place for localization accuracy and 2nd place overall (overall score is an average of accuracy and other qualititative metrics from a panel of judges).
Wednesday, December 19, 2012
Two person tracking in an apartment using radio tomographic vision
Two person tracking in an apartment using radio tomographic vision Tube. Duration : 0.98 Mins.
This video shows the experimental results from a radio tomography (RT)-based two-person tracking experiment in an apartment. The people are not wearing any radio tags, instead, a wireless network of 33 (IEEE 802.15.4) transceivers deployed in the apartment measure changes in received signal strength (RSS), known as "the number of bars", caused by the people. Based on which links experience changes, the RT algorithm comes up with an image (shown at left) that has highest values (red) where it guesses that a person is located, and lowest values (blue) where it guesses that no person is located. A multi-target tracking algorithm developed by Dr. Maurizio Bocca at the University of Utah identifies from the image where the "blobs" are and what path they are taking through the apartment, using computer vision methods adapted to the RT problem. On the left, the video shows the apartment with black indicating walls, grey indicating furniture, white circle indicating the actual person location, and white X indicating the current estimate of the person. Dr. Bocca's algorithm track the two people to within an average error of about 30 cm (1 foot). Dr. Bocca and his University of Utah team used this system to compete in the EvAAL 2012 tracking competition and win 1st place for localization accuracy and 2nd place overall (overall score is an average of accuracy and other qualititative metrics from a panel of judges).
This video shows the experimental results from a radio tomography (RT)-based two-person tracking experiment in an apartment. The people are not wearing any radio tags, instead, a wireless network of 33 (IEEE 802.15.4) transceivers deployed in the apartment measure changes in received signal strength (RSS), known as "the number of bars", caused by the people. Based on which links experience changes, the RT algorithm comes up with an image (shown at left) that has highest values (red) where it guesses that a person is located, and lowest values (blue) where it guesses that no person is located. A multi-target tracking algorithm developed by Dr. Maurizio Bocca at the University of Utah identifies from the image where the "blobs" are and what path they are taking through the apartment, using computer vision methods adapted to the RT problem. On the left, the video shows the apartment with black indicating walls, grey indicating furniture, white circle indicating the actual person location, and white X indicating the current estimate of the person. Dr. Bocca's algorithm track the two people to within an average error of about 30 cm (1 foot). Dr. Bocca and his University of Utah team used this system to compete in the EvAAL 2012 tracking competition and win 1st place for localization accuracy and 2nd place overall (overall score is an average of accuracy and other qualititative metrics from a panel of judges).
Subscribe to:
Post Comments (Atom)
0 comments:
Post a Comment