A fuzzy approach for early human action detection / Ekta Vats

Ekta, Vats (2016) A fuzzy approach for early human action detection / Ekta Vats. PhD thesis, University of Malaya.

Preview

PDF (Thesis PhD) Download (11Mb) | Preview

Abstract

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. iii 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.

Actions (For repository staff only : Login required)