A fuzzy approach for early human action detection / Ekta Vats
Early human action detection is an important computer vision task with a wide spectrum of potential applications. Most existing methods deal with the detection of an action after its completion. Contrarily, for early detection it is essential to detect an action as early as possible. Therefore, t...
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| Format: | Thesis |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/6607/1/ekta.pdf http://studentsrepo.um.edu.my/6607/ |
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| Summary: | Early human action detection is an important computer vision task with a wide
spectrum of potential applications. Most existing methods deal with the detection of an
action after its completion. Contrarily, for early detection it is essential to detect an action
as early as possible. Therefore, this thesis develops a solution to detect ongoing human
action as soon as it begins, but before it finishes.
In order to perform early human action detection, the conventional classification
problem is modified into frame-by-frame level classification. There exists well-known
classifiers such as Support Vector Machines (SVM), K-nearest Neighbour (KNN), etc. to
perform action classification. However, the employability of these algorithms depends
on the desired application and its requirements. Therefore, selection of the classifier to
employ for the classification task is an important issue to be taken into account. The
first part of the thesis studies this problem and fuzzy Bandler-Kohout (BK) sub-triangle
product (subproduct) is employed as a classifier. The performance is tested for human
action recognition and scene classification. This is a crucial step as it is the first attempt
of using fuzzy BK subproduct for classification.
The second part of this thesis studies the problem of early human action detection.
The method proposed is based on fuzzy BK subproduct inference mechanism and utilizes
the fuzzy capabilities in handling the uncertainties that exist in the real-world for reliable
decision making. The fuzzy membership function generated frame-by-frame from fuzzy
BK subproduct provides the basis to detect an action before it is completed, when a certain
threshold is attained in a suitable way. In order to test the effectiveness of the proposed
framework, a set of experiments is performed for few action sequences where the detector
is able to recognize an action upon seeing �32% of the frames.
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Finally, the proposed method is analyzed from a broader perspective and a hybrid
technique for early anticipation of human action is proposed. It combines the benefits of
computer vision and fuzzy set theory based on fuzzy BK subproduct. The novelty lies
in the construction of a frame-by-frame membership function for each kind of possible
movement, taking into account several human actions from a publicly available dataset.
Furthermore, the impact of various fuzzy implication operators and inference structures
in retrieving the relationship between the human subject and the actions performed is
discussed. The existing fuzzy implication operators are capable of handling only two dimensional
data. A third dimension ‘time’ plays a crucial role in human action recognition
to model the human movement changes over time. Therefore, a new space-time fuzzy
implication operator is introduced, by modifying the existing implication operators to
accommodate time as an added dimension. Empirically, the proposed hybrid technique
is efficiently able to detect an action before completion and outperform the conventional
solutions with good detection rate. The detector is able to identify an action upon viewing
�23% of the frames on an average. |
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