Authors:
Andre Brandao
1
;
Leandro A. F. Fernandes
2
and
Esteban Clua
2
Affiliations:
1
Universidade Federal Fluminense and German Research Center for Artificial Intelligence (DFKI GmbH), Brazil
;
2
Universidade Federal Fluminense, Brazil
Keyword(s):
Body-part Detection, Tracking, Pose Classification, AGEX, ASIFT, Depth Images, Medial Axis.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Shape Representation and Matching
Abstract:
The automatic detection and tracking of human body parts in color images is highly sensitive to appearance features such as illumination, skin color and clothes. As a result, the use of depth images has been shown to be an attractive alternative over color images due to its invariance to lighting conditions. However, body part detection and tracking is still a challenging problem, mainly because the shape and depth of the imaged body can change depending on the perspective. We present a hybrid approach, called M5AIE, that uses both color and depth information to perform body part detection, tracking and pose classification. We have developed a modified Accumulative Geodesic Extrema (AGEX) approach for detecting body part candidates. We also have used the Affine-SIFT (ASIFT) algorithm for feature extraction, and we have adapted the conventional matching method to perform tracking and labeling of body parts in a sequence of images that has color and depth information. The results produ
ced by our tracking system were used with the C4.5 Gain Ratio Decision Tree, the naïve Bayes and the KNN classification algorithms for the identification of the users pose.
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