47 research outputs found
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in
similar poses nearby. This embedding can be used as a direct method of
comparing images based on human pose, avoiding potential challenges of
estimating body joint positions. Pose embedding learning is formulated under a
triplet-based distance criterion. A deep architecture is used to allow learning
of a representation capable of making distinctions between different poses.
Experiments on human pose matching and retrieval from video data demonstrate
the potential of the method
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
We conduct an in-depth exploration of different strategies for doing event
detection in videos using convolutional neural networks (CNNs) trained for
image classification. We study different ways of performing spatial and
temporal pooling, feature normalization, choice of CNN layers as well as choice
of classifiers. Making judicious choices along these dimensions led to a very
significant increase in performance over more naive approaches that have been
used till now. We evaluate our approach on the challenging TRECVID MED'14
dataset with two popular CNN architectures pretrained on ImageNet. On this
MED'14 dataset, our methods, based entirely on image-trained CNN features, can
outperform several state-of-the-art non-CNN models. Our proposed late fusion of
CNN- and motion-based features can further increase the mean average precision
(mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the
state-of-the-art classification performance on the challenging UCF-101 dataset
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks
Infrared (IR) imaging has the potential to enable more robust action
recognition systems compared to visible spectrum cameras due to lower
sensitivity to lighting conditions and appearance variability. While the action
recognition task on videos collected from visible spectrum imaging has received
much attention, action recognition in IR videos is significantly less explored.
Our objective is to exploit imaging data in this modality for the action
recognition task. In this work, we propose a novel two-stream 3D convolutional
neural network (CNN) architecture by introducing the discriminative code layer
and the corresponding discriminative code loss function. The proposed network
processes IR image and the IR-based optical flow field sequences. We pretrain
the 3D CNN model on the visible spectrum Sports-1M action dataset and finetune
it on the Infrared Action Recognition (InfAR) dataset. To our best knowledge,
this is the first application of the 3D CNN to action recognition in the IR
domain. We conduct an elaborate analysis of different fusion schemes (weighted
average, single and double-layer neural nets) applied to different 3D CNN
outputs. Experimental results demonstrate that our approach can achieve
state-of-the-art average precision (AP) performances on the InfAR dataset: (1)
the proposed two-stream 3D CNN achieves the best reported 77.5% AP, and (2) our
3D CNN model applied to the optical flow fields achieves the best reported
single stream 75.42% AP
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Modeling videos and image-sets as linear subspaces has proven beneficial for
many visual recognition tasks. However, it also incurs challenges arising from
the fact that linear subspaces do not obey Euclidean geometry, but lie on a
special type of Riemannian manifolds known as Grassmannian. To leverage the
techniques developed for Euclidean spaces (e.g, support vector machines) with
subspaces, several recent studies have proposed to embed the Grassmannian into
a Hilbert space by making use of a positive definite kernel. Unfortunately,
only two Grassmannian kernels are known, none of which -as we will show- is
universal, which limits their ability to approximate a target function
arbitrarily well. Here, we introduce several positive definite Grassmannian
kernels, including universal ones, and demonstrate their superiority over
previously-known kernels in various tasks, such as classification, clustering,
sparse coding and hashing
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