36,538 research outputs found
Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
Action detection and temporal segmentation of actions in videos are topics of
increasing interest. While fully supervised systems have gained much attention
lately, full annotation of each action within the video is costly and
impractical for large amounts of video data. Thus, weakly supervised action
detection and temporal segmentation methods are of great importance. While most
works in this area assume an ordered sequence of occurring actions to be given,
our approach only uses a set of actions. Such action sets provide much less
supervision since neither action ordering nor the number of action occurrences
are known. In exchange, they can be easily obtained, for instance, from
meta-tags, while ordered sequences still require human annotation. We introduce
a system that automatically learns to temporally segment and label actions in a
video, where the only supervision that is used are action sets. An evaluation
on three datasets shows that our method still achieves good results although
the amount of supervision is significantly smaller than for other related
methods.Comment: CVPR 201
A robust and efficient video representation for action recognition
This paper introduces a state-of-the-art video representation and applies it
to efficient action recognition and detection. We first propose to improve the
popular dense trajectory features by explicit camera motion estimation. More
specifically, we extract feature point matches between frames using SURF
descriptors and dense optical flow. The matches are used to estimate a
homography with RANSAC. To improve the robustness of homography estimation, a
human detector is employed to remove outlier matches from the human body as
human motion is not constrained by the camera. Trajectories consistent with the
homography are considered as due to camera motion, and thus removed. We also
use the homography to cancel out camera motion from the optical flow. This
results in significant improvement on motion-based HOF and MBH descriptors. We
further explore the recent Fisher vector as an alternative feature encoding
approach to the standard bag-of-words histogram, and consider different ways to
include spatial layout information in these encodings. We present a large and
varied set of evaluations, considering (i) classification of short basic
actions on six datasets, (ii) localization of such actions in feature-length
movies, and (iii) large-scale recognition of complex events. We find that our
improved trajectory features significantly outperform previous dense
trajectories, and that Fisher vectors are superior to bag-of-words encodings
for video recognition tasks. In all three tasks, we show substantial
improvements over the state-of-the-art results
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
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
We present an approach for weakly supervised learning of human actions. Given
a set of videos and an ordered list of the occurring actions, the goal is to
infer start and end frames of the related action classes within the video and
to train the respective action classifiers without any need for hand labeled
frame boundaries. To address this task, we propose a combination of a
discriminative representation of subactions, modeled by a recurrent neural
network, and a coarse probabilistic model to allow for a temporal alignment and
inference over long sequences. While this system alone already generates good
results, we show that the performance can be further improved by approximating
the number of subactions to the characteristics of the different action
classes. To this end, we adapt the number of subaction classes by iterating
realignment and reestimation during training. The proposed system is evaluated
on two benchmark datasets, the Breakfast and the Hollywood extended dataset,
showing a competitive performance on various weak learning tasks such as
temporal action segmentation and action alignment
Action recognition based on efficient deep feature learning in the spatio-temporal domain
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.Peer ReviewedPostprint (author's final draft
Video Time: Properties, Encoders and Evaluation
Time-aware encoding of frame sequences in a video is a fundamental problem in
video understanding. While many attempted to model time in videos, an explicit
study on quantifying video time is missing. To fill this lacuna, we aim to
evaluate video time explicitly. We describe three properties of video time,
namely a) temporal asymmetry, b)temporal continuity and c) temporal causality.
Based on each we formulate a task able to quantify the associated property.
This allows assessing the effectiveness of modern video encoders, like C3D and
LSTM, in their ability to model time. Our analysis provides insights about
existing encoders while also leading us to propose a new video time encoder,
which is better suited for the video time recognition tasks than C3D and LSTM.
We believe the proposed meta-analysis can provide a reasonable baseline to
assess video time encoders on equal grounds on a set of temporal-aware tasks.Comment: 14 pages, BMVC 201
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