Articulated Models for Human Motion Analysis - Image Processing

176 Pages·2012·2.47 MB·English

Universitat Polit`ecnica de Catalunya
Articulated Models for Human
Motion Analysis
Ph.D. Thesis
Author:
Adolfo L´opez M´endez
Thesis Advisor:
Josep R. Casas
October 2012

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A la meva fam´ılia
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“All our knowledge has its origins in our perceptions.”
Leonardo da Vinci

Summary
Humanmotionanalysisisasabroadareaofcomputervisionthathasstronglyattracted
the interest of researchers in the last decades. Motion analysis covers topics such as
humanmotiontrackingandestimation,actionandbehaviorrecognitionorsegmentation
of human motion. All these fields are challenging due to different reasons, but mostly
because of viewing perspectives, clutter and the imprecise semantics of actions and
human motion.
Thecomputervisioncommunityhasaddressedhumanmotionanalysisfromseveralper-
spectives. Earlierapproachesoftenreliedonarticulatedhumanbodymodelsrepresented
inthethree-dimensionalworld. However,duetothetraditionallyhighdifficultyandcost
of estimating such an articulated structure from video, research has focus on the devel-
opment of human motion analysis approaches relying on low-level features. Although
obtaining impressive results in several tasks, low-level features are typically conditioned
byappearanceandviewpoint, thusmakingdifficulttheirapplicationondifferentscenar-
ios. Nonetheless, the increase in computational power, the massive availability of data
and the irruption of consumer-depth cameras is changing the scenario, and with that
change human motion analysis through articulated models can be reconsidered.
Analyzing and understanding of human motion through 3-dimensional information is
still a crucial issue in order to obtain richer models of dynamics and behavior. In that
sense, articulated models of the human body offer a compact and view-invariant repre-
sentation of motion that can be used to leverage motion analysis. In this dissertation,
wepresentseveralapproachesformotionanalysis. Inparticular, weaddresstheproblem
of pose inference, action recognition and temporal clustering of human motion. Artic-
ulated models are the leitmotiv in all the presented approaches. Firstly, we address
pose inference by formulating a layered analysis-by-synthesis framework where models
are used to generate hypothesis that are matched against video. Based on the same ar-
ticulated representation upon which models are built, we propose an action recognition
framework. Actions are seen as time-series observed through the articulated model and
generated by underlying dynamical systems that we hypothesize that are generating the
time-series. Such an hypothesis is used in order to develop recognition methods based
on time-delay embeddings, which are analysis tools that do not make assumptions on
the form of the form of the underlying dynamical system. Finally, we propose a method
to cluster human motion sequences into distinct behaviors, without a priori knowledge
of the number of actions in the sequence. Our approach relies on the articulated model
representation in order to learn a distance metric from pose data. This metric aims at
capturing semantics from labeled data in order to cluster unseen motion sequences into
meaningful behaviors. The proposed approaches are evaluated using publicly available
datasets in order to objectively measure our contributions.