Authors:
Tanwi Mallick
;
Ankit Khedia
;
Partha Pratim Das
and
Arun Kumar Majumdar
Affiliation:
Indian Institute of Technology, India
Keyword(s):
Gait Recognition, Kinect Skeleton Stream.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
Recognizing persons from gait has attracted attention in computer vision research for over a decade and a
half. To extract the motion information in gait, researchers have either used wearable markers or RGB videos.
Markers naturally offer good accuracy and reliability but has the disadvantage of being intrusive and expensive.
RGB images, on the other hand, need high processing time to achieve good accuracy. Advent of low-cost depth
data from Kinect 1.0 and its human-detection and skeleton-tracking abilities have opened new opportunities
in gait recognition. Using skeleton data it gets cheaper and easier to get the body-joint information that can
provide critical clue to gait-related motions. In this paper, we attempt to use the skeleton stream from Kinect
1.0 for gait recognition. Various types of gait features are extracted from the joint-points in the stream and
the appropriate classifiers are used to compute effective matching scores. To test our system and compare
performance, w
e create a benchmark data set of 5 walks each for 29 subjects and implement a state-of-the-art
gait recognizer for RGB videos. Tests show a moderate accuracy of 65% for our system. This is low compared
to the accuracy of RGB-based method (which achieved 83% on the same data set) but high compared to
similar skeleton-based approaches (usually below 50%). Further we compare execution time of various parts
of our system to highlight efficiency advantages of our method and its potential as a real-time recogniser if an
optimized implementation can be done.
(More)