14,243 research outputs found
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
Machine Analysis of Facial Expressions
No abstract
Beat-Event Detection in Action Movie Franchises
While important advances were recently made towards temporally localizing and
recognizing specific human actions or activities in videos, efficient detection
and classification of long video chunks belonging to semantically defined
categories such as "pursuit" or "romance" remains challenging.We introduce a
new dataset, Action Movie Franchises, consisting of a collection of Hollywood
action movie franchises. We define 11 non-exclusive semantic categories -
called beat-categories - that are broad enough to cover most of the movie
footage. The corresponding beat-events are annotated as groups of video shots,
possibly overlapping.We propose an approach for localizing beat-events based on
classifying shots into beat-categories and learning the temporal constraints
between shots. We show that temporal constraints significantly improve the
classification performance. We set up an evaluation protocol for beat-event
localization as well as for shot classification, depending on whether movies
from the same franchise are present or not in the training data
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