Abstract
The bag of words is a popular and successful method for human activity recognition. This method usually uses visual based sparse features for activity classification. It is also known that movement has useful clues for activity detection, but sparse features usually miss this vital piece of information. Two-dimensional image planar motion information is easy to extract but it is very dependant on depth and calibration parameters. Three-dimensional motion is rich in information and can be calculated from active cameras or multiple passive cameras, but it restricts the applicability of the method. To overcome these issues, we have proposed the use of disparity maps, which are relatively easy to extract from stereo videos and are more informative than 2D image planar motion information. In this work, we have combined the motion information and disparity maps to introduce a new sparse feature descriptor that encodes motion information, instead of visual information.
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Notes
- 1.
More accurately by the number of times a word appeared in a document compared to the number of times it appeared in other documents.
- 2.
The distance between two center of projections.
- 3.
The distance between center of projection and the image plane.
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Habashi, P., Boufama, B., Ahmad, I.S. (2015). The Bag of Micro-Movements for Human Activity Recognition. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_29
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