Statistical Machine Learning for Human Behaviour Analysis

• Autore: Moeslund Thomas
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (300 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: 3D convolutional neural networks; accuracy; action recognition; adaptive classifiers; age classification; attention allocation; attention behavior; biometric recognition; blurring detection; body movements; boundary segmentation; categorical data; committee of classifiers; concept drift; context-aware framework; convolutional neural network; deep learning; discrete stationary wavelet transform; emotion recognition; Empatica E4; ensemble methods; face analysis; face segmentation; false negative rate; fibromyalgia; fingerprint image enhancement; fingerprint quality; foggy image; frequency domain; gait event; gender classification; gestures; hand sign language; head pose estimation; hybrid entropy; individual behavior estimation; information entropy; interpretable machine learning; k-means clustering; Kinect sensor; Learning Using Concave and Convex Kernels; multi-modal; multi-objective evolutionary algorithms; multimodal-based human identification; neural networks; noisy image; object contour detection; privacy; privacy-aware; profoundly deaf; recurrent concepts; restricted Boltzmann machine (RBM); rule-based classifiers; saliency detection; self-reported survey; silhouettes difference; single pixel single photon image acquisition; singular point detection; spatial domain; spectrograms; speech; speech emotion recognition; statistical-based time-frequency domain and crowd condition; stock price direction prediction; time-of-flight; toe-off detection
• Persona (resp. second.): EscaleraSergio; AnbarjafariGholamreza; NasrollahiKamal; WanJun; MoeslundThomas
• Sommario/riassunto: This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
• Titolo autorizzato: Statistical Machine Learning for Human Behaviour Analysis
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557288403321
• Lo trovi qui: Univ. Federico II