26,956 research outputs found
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Human activity understanding with 3D/depth sensors has received increasing
attention in multimedia processing and interactions. This work targets on
developing a novel deep model for automatic activity recognition from RGB-D
videos. We represent each human activity as an ensemble of cubic-like video
segments, and learn to discover the temporal structures for a category of
activities, i.e. how the activities to be decomposed in terms of
classification. Our model can be regarded as a structured deep architecture, as
it extends the convolutional neural networks (CNNs) by incorporating structure
alternatives. Specifically, we build the network consisting of 3D convolutions
and max-pooling operators over the video segments, and introduce the latent
variables in each convolutional layer manipulating the activation of neurons.
Our model thus advances existing approaches in two aspects: (i) it acts
directly on the raw inputs (grayscale-depth data) to conduct recognition
instead of relying on hand-crafted features, and (ii) the model structure can
be dynamically adjusted accounting for the temporal variations of human
activities, i.e. the network configuration is allowed to be partially activated
during inference. For model training, we propose an EM-type optimization method
that iteratively (i) discovers the latent structure by determining the
decomposed actions for each training example, and (ii) learns the network
parameters by using the back-propagation algorithm. Our approach is validated
in challenging scenarios, and outperforms state-of-the-art methods. A large
human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary
version was published in ACM MM'14 conferenc
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Semantic annotations are vital for training models for object recognition,
semantic segmentation or scene understanding. Unfortunately, pixelwise
annotation of images at very large scale is labor-intensive and only little
labeled data is available, particularly at instance level and for street
scenes. In this paper, we propose to tackle this problem by lifting the
semantic instance labeling task from 2D into 3D. Given reconstructions from
stereo or laser data, we annotate static 3D scene elements with rough bounding
primitives and develop a model which transfers this information into the image
domain. We leverage our method to obtain 2D labels for a novel suburban video
dataset which we have collected, resulting in 400k semantic and instance image
annotations. A comparison of our method to state-of-the-art label transfer
baselines reveals that 3D information enables more efficient annotation while
at the same time resulting in improved accuracy and time-coherent labels.Comment: 10 pages in Conference on Computer Vision and Pattern Recognition
(CVPR), 201
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique
for the task of 3D keypoint prediction from a single depth scan or image. Our
key idea is to utilize the fact that predictions from different views of the
same or similar objects should be consistent with each other. Such view
consistency can provide effective regularization for keypoint prediction on
unlabeled instances. In addition, we introduce a geometric alignment term to
regularize predictions in the target domain. The resulting loss function can be
effectively optimized via alternating minimization. We demonstrate the
effectiveness of our approach on real datasets and present experimental results
showing that our approach is superior to state-of-the-art general-purpose
domain adaptation techniques.Comment: ECCV 201
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