Machine Learning for Vision-Based Motion Analysis Theory and Techniques
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
London
Springer London
2011, 2011
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| Edition: | 1st ed. 2011 |
| Series: | Advances in Computer Vision and Pattern Recognition
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Part I: Manifold Learning and Clustering/Segmentation
- Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis
- Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis
- Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models
- Part II: Tracking
- Mixed-state Markov Models in Image Motion Analysis
- Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate
- Discriminative Multiple Target Tracking
- A Framework of Wire Tracking in Image Guided Interventions
- Part III: Motion Analysis and Behavior Modeling
- An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos
- Video-based Human Motion Estimation by Part-whole Gait Manifold Learning
- Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes
- Learning Behavioral Patterns of Time Series for Video-surveillance
- Part IV: Gesture and Action Recognition
- Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs
- Learning Transferable Distance Functions for Human Action Recognition