The automatic recognition of human actions in video is of great interest in many applications
such as automated surveillance, content-based video summarization, video search, and indexing.
The problem is challenging due to a wide range of variations among the motion pattern of a given
action such as walking across different subjects and the low variations among similar motions
such as running and jogging.
This thesis has three contributions in a discriminative bottom-up framework to improve the
multi-resolution analysis of the motion patterns in video for better recognition of human actions.
The first contribution of this thesis is the introduction of a novel approach for a robust local
motion feature detection in video. To this end, four different multi-resolution temporally
causal and asymmetric filters of log Gaussian, scale-derivative Gaussian, Poisson, and asymmetric
sinc are introduced. The performance of these filters is compared with the widely used
multi-resolution Gabor filter in a common framework for detection of local salient motions. The
features obtained from the asymmetric filtering are more precise and more robust under geometric
deformations such as view change or affine transformations. Moreover, they provide higher
classification accuracy when they are used with a standard bag-of-words representation of actions
and a single discriminative classifier. The experimental results show that the asymmetric
sinc performs the best. The Poisson and the scale-derivative Gaussian perform better than log
Gaussian and that better than the symmetric temporal Gabor filter.
The second contribution of this thesis is the introduction of an efficient action representation.
The observation is that the salient features at different spatial and temporal scales characterize
different motion information. A multi-resolution analysis of the motion characteristic should be
representative of different actions. A multi-resolution action signature provides a more discriminative
video representation.
The third contribution of this thesis is on the classification of different human actions. To this
end, an ensemble of classifiers in a multiple classifier systems (MCS) framework with a parallel
topology is utilized. This framework can fully benefit from the multi-resolution characteristics
of the motion patterns in the human actions. The classification combination concept of the MCS
has been then extended to address two problems in the configuration setting of a recognition
framework, namely the choice of distance metric for comparing the action representations and
the size of the codebook by which an action is represented. This implication of MCS at multiple
stages of the recognition pipeline provides a multi-stage MCS framework which outperforms the
existing methods which use a single classifier.
Based on the experimental results of the local feature detection and the action classification,
the multi-stage MCS framework, which uses the multi-scale features obtained from the temporal
asymmetric sinc filtering, is recommended for the task of human action recognition in video.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/6346 |
| Date | January 2011 |
| Creators | Shabani, Hossein |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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