42,095 research outputs found
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
DAP3D-Net: Where, What and How Actions Occur in Videos?
Action parsing in videos with complex scenes is an interesting but
challenging task in computer vision. In this paper, we propose a generic 3D
convolutional neural network in a multi-task learning manner for effective Deep
Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase,
action localization, classification and attributes learning can be jointly
optimized on our appearancemotion data via DAP3D-Net. For an upcoming test
video, we can describe each individual action in the video simultaneously as:
Where the action occurs, What the action is and How the action is performed. To
well demonstrate the effectiveness of the proposed DAP3D-Net, we also
contribute a new Numerous-category Aligned Synthetic Action dataset, i.e.,
NASA, which consists of 200; 000 action clips of more than 300 categories and
with 33 pre-defined action attributes in two hierarchical levels (i.e.,
low-level attributes of basic body part movements and high-level attributes
related to action motion). We learn DAP3D-Net using the NASA dataset and then
evaluate it on our collected Human Action Understanding (HAU) dataset.
Experimental results show that our approach can accurately localize, categorize
and describe multiple actions in realistic videos
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