Riemannian Manifold-Based Modeling and Classification Methods for Video Activities with Applications to Assisted Living and Smart Home
Doktorsavhandling, 2016

This thesis mainly focuses on visual-information based daily activity classification, anomaly detection, and video tracking through using visual sensors. The main reasons for adopting visual-information based methods are due to: (i) vision plays a major role in recognition/classification of activities which is a fundamental issue in a human-centric system; (ii) visual sensor-based analysis may possibly offer high performance with minimum disturbance to individuals' daily lives. Manifolds are employed for efficient modeling and low-dimensional representation of video activities, due to the following reasons: (a) the nonlinear nature of manifolds enables effective description of dynamic processes of human activities involving non-planar movement, which lie on a nonlinear manifold other than a vector space; (b) many video features of human activities may be effectively described by low-dimensional data points on the Riemannian manifold while still maintaining the important property such as topology and geometry; (c) the Riemannian geometry provides a way to measure the distances/dissimilarities between different activities on the nonlinear manifold, hence is a suitable tool for classification and tracking. In this thesis, six different methods for visual analysis of human activities are introduced, including fall detection in video, activity classification in image and video, and video tracking using single camera and multiple cameras. Considering the contribution in theoretical aspects, the use of Riemannian manifolds was investigated for mathematical modeling of video activities, and new methods were developed for characterizing and distinguishing different activities. Experiments on real-world video/image datasets were conducted to evaluate the performance of each method. Results, comparisons, and evaluations showed that the methods achieved state-of-the-art performance. From the perspective of application, the methods have a wide range of potential applications such as assisted living, smart homes, eHealthcare, smart vehicles, office automation, safety systems and services, security systems, situation-aware human-computer interfaces, robot learning, etc.

assisted living

Riemannian manifold

fall detection

smart homes

activities of daily living (ADL)

video tracking

activity classification

EB, Hörsalsvägen 11, Chalmers
Opponent: Prof. Michael Felsberg, Linköping University, Sweden

Författare

Yixiao Yun

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Riemannian Manifold-Based Support Vector Machine for Human Activity Classification in Images

IEEE International Conference on Image Processing (ICIP 2013), Sept.15 - 18, Melbourne, Australia,; (2013)p. 3466-3469

Paper i proceeding

Visual Object Tracking with Online Learning on Riemannian Manifolds by One-Class Support Vector Machines

IEEE International Conference on Image Processing (ICIP 2014), Oct.27 - 30, 2014, Paris, France,; (2014)p. 1902-1906

Paper i proceeding

Visual information-based activity recognition and fall detection for assisted living and ehealthcare

Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control,; (2016)p. 395-425

Kapitel i bok

Exploiting Riemannian Manifolds for Daily Activity Classification in Video Towards Health Care

IEEE International Conference on E-health Networking, Application & Services (HealthCom 2016), Munich, Germany, Sept. 14-17, 2016.,; (2016)p. 363-368

Paper i proceeding

Multi-View ML Object Tracking with Online Learning on Riemannian Manifolds by Combining Geometric Constraints

IEEE Journal on Emerging and Selected Topics in Circuits and Systems,; Vol. 3(2013)p. 12 -

Artikel i vetenskaplig tidskrift

Part-based features and geodesic-induced kernel machine for human activity classification on Riemannian manifolds

Visual information-based activity recognition and fall detection for assisted living and ehealthcare (Chapter 15, Elsevier book on Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control)

Time-dependent bag of words on manifolds for geodesic-based classification of video activities

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Robotteknik och automation

Signalbehandling

Datorseende och robotik (autonoma system)

ISBN

978-91-7597-421-7

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4102

Utgivare

Chalmers tekniska högskola

EB, Hörsalsvägen 11, Chalmers

Opponent: Prof. Michael Felsberg, Linköping University, Sweden

Mer information

Skapat

2016-09-20