16,303 research outputs found
P-CNN: Pose-based CNN Features for Action Recognition
This work targets human action recognition in video. While recent methods
typically represent actions by statistics of local video features, here we
argue for the importance of a representation derived from human pose. To this
end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN)
for action recognition. The descriptor aggregates motion and appearance
information along tracks of human body parts. We investigate different schemes
of temporal aggregation and experiment with P-CNN features obtained both for
automatically estimated and manually annotated human poses. We evaluate our
method on the recent and challenging JHMDB and MPII Cooking datasets. For both
datasets our method shows consistent improvement over the state of the art.Comment: ICCV, December 2015, Santiago, Chil
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Existing methods to recognize actions in static images take the images at
their face value, learning the appearances---objects, scenes, and body
poses---that distinguish each action class. However, such models are deprived
of the rich dynamic structure and motions that also define human activity. We
propose an approach that hallucinates the unobserved future motion implied by a
single snapshot to help static-image action recognition. The key idea is to
learn a prior over short-term dynamics from thousands of unlabeled videos,
infer the anticipated optical flow on novel static images, and then train
discriminative models that exploit both streams of information. Our main
contributions are twofold. First, we devise an encoder-decoder convolutional
neural network and a novel optical flow encoding that can translate a static
image into an accurate flow map. Second, we show the power of hallucinated flow
for recognition, successfully transferring the learned motion into a standard
two-stream network for activity recognition. On seven datasets, we demonstrate
the power of the approach. It not only achieves state-of-the-art accuracy for
dense optical flow prediction, but also consistently enhances recognition of
actions and dynamic scenes.Comment: Published in CVPR 2018, project page:
http://vision.cs.utexas.edu/projects/im2flow
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Expanded Parts Model for Semantic Description of Humans in Still Images
We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.Comment: Accepted for publication in IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI
Second-order Temporal Pooling for Action Recognition
Deep learning models for video-based action recognition usually generate
features for short clips (consisting of a few frames); such clip-level features
are aggregated to video-level representations by computing statistics on these
features. Typically zero-th (max) or the first-order (average) statistics are
used. In this paper, we explore the benefits of using second-order statistics.
Specifically, we propose a novel end-to-end learnable feature aggregation
scheme, dubbed temporal correlation pooling that generates an action descriptor
for a video sequence by capturing the similarities between the temporal
evolution of clip-level CNN features computed across the video. Such a
descriptor, while being computationally cheap, also naturally encodes the
co-activations of multiple CNN features, thereby providing a richer
characterization of actions than their first-order counterparts. We also
propose higher-order extensions of this scheme by computing correlations after
embedding the CNN features in a reproducing kernel Hilbert space. We provide
experiments on benchmark datasets such as HMDB-51 and UCF-101, fine-grained
datasets such as MPII Cooking activities and JHMDB, as well as the recent
Kinetics-600. Our results demonstrate the advantages of higher-order pooling
schemes that when combined with hand-crafted features (as is standard practice)
achieves state-of-the-art accuracy.Comment: Accepted in the International Journal of Computer Vision (IJCV
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