Title :
Kinsight: Localizing and Tracking Household Objects Using Depth-Camera Sensors
Author :
Nirjon, Shahriar ; Stankovic, John A.
Author_Institution :
Dept. of Comput. Sci., Univ. of Virginia, Charlottesville, VA, USA
Abstract :
We solve the problem of localizing and tracking household objects using a depth-camera sensor network. We design and implement Kin sight that tracks household objects indirectly -- by tracking human figures, and detecting and recognizing objects from human-object interactions. We devise two novel algorithms: (1) Depth Sweep -- that uses depth information to efficiently extract objects from an image, and (2) Context Oriented Object Recognition -- that uses location history and activity context along with an RGB image to recognize object sat home. We thoroughly evaluate Kinsight´s performance with a rich set of controlled experiments. We also deploy Kinsightin real-world scenarios and show that it achieves an average localization error of about 13 cm.
Keywords :
cameras; image sensors; object detection; Kinsight performance; RGB image; context oriented object recognition; depth sweep; depth-camera sensor network; household objects tracking; human-object interactions; localization error; objects detection; tracking human figures; Algorithm design and analysis; Context; Databases; Humans; Image segmentation; Object recognition; Real time systems;
Conference_Titel :
Distributed Computing in Sensor Systems (DCOSS), 2012 IEEE 8th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-1693-4
DOI :
10.1109/DCOSS.2012.27