Part I: Manifold Learning and Clustering/SegmentationPractical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion AnalysisTomoya Sakai, and Atsushi ImiyaRiemannian Manifold Clustering and Dimensionality Reduction for Vision-based AnalysisAlvina GohManifold Learning for Multi-dimensional Auto-regressive Dynamical ModelsFabio CuzzolinPart II: TrackingMixed-state Markov Models in Image Motion AnalysisTomás Crivelli, Patrick Bouthemy, Bruno Cernuschi Frías, and Jian-feng YaoLearning to Detect Event Sequences in Surveillance Streams at Very Low Frame RatePaolo Lombardi, and Cristina VersinoDiscriminative Multiple Target TrackingXiaoyu Wang, Gang Hua, and Tony X. HanA Framework of Wire Tracking in Image Guided InterventionsPeng Wang, Andreas Meyer, Terrence Chen, Shaohua K. Zhou, and Dorin ComaniciuPart III: Motion Analysis and Behavior ModelingAn Integrated Approach to Visual Attention Modeling for Saliency Detection in VideosSunaad Nataraju, Vineeth Balasubramanian, and Sethuraman PanchanathanVideo-based Human Motion Estimation by Part-whole Gait Manifold LearningGuoliang Fan, and Xin ZhangSpatio-temporal Motion Pattern Models of Extremely Crowded ScenesLouis Kratz and Ko NishinoLearning Behavioral Patterns of Time Series for Video-surveillanceNicoletta Noceti, Matteo Santoro, and Francesca OdonePart IV: Gesture and Action RecognitionRecognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMsDaniel Kelly, John Mc Donald and Charles MarkhamLearning Transferable Distance Functions for Human Action RecognitionWeilong Yang, YangWang, and Greg Mori
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 understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition.
Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions.
Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small tr
Provides a comprehensive and accessible review of vision-based motion analysis
Highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective
Describes the benefits of collaboration between the communities of object motion understanding and machine learning