25,882 research outputs found
Generalized Rank Pooling for Activity Recognition
Most popular deep models for action recognition split video sequences into
short sub-sequences consisting of a few frames; frame-based features are then
pooled for recognizing the activity. Usually, this pooling step discards the
temporal order of the frames, which could otherwise be used for better
recognition. Towards this end, we propose a novel pooling method, generalized
rank pooling (GRP), that takes as input, features from the intermediate layers
of a CNN that is trained on tiny sub-sequences, and produces as output the
parameters of a subspace which (i) provides a low-rank approximation to the
features and (ii) preserves their temporal order. We propose to use these
parameters as a compact representation for the video sequence, which is then
used in a classification setup. We formulate an objective for computing this
subspace as a Riemannian optimization problem on the Grassmann manifold, and
propose an efficient conjugate gradient scheme for solving it. Experiments on
several activity recognition datasets show that our scheme leads to
state-of-the-art performance.Comment: Accepted at IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR), 201
What Will I Do Next? The Intention from Motion Experiment
In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
Workshop
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Continuous Action Recognition Based on Sequence Alignment
Continuous action recognition is more challenging than isolated recognition
because classification and segmentation must be simultaneously carried out. We
build on the well known dynamic time warping (DTW) framework and devise a novel
visual alignment technique, namely dynamic frame warping (DFW), which performs
isolated recognition based on per-frame representation of videos, and on
aligning a test sequence with a model sequence. Moreover, we propose two
extensions which enable to perform recognition concomitant with segmentation,
namely one-pass DFW and two-pass DFW. These two methods have their roots in the
domain of continuous recognition of speech and, to the best of our knowledge,
their extension to continuous visual action recognition has been overlooked. We
test and illustrate the proposed techniques with a recently released dataset
(RAVEL) and with two public-domain datasets widely used in action recognition
(Hollywood-1 and Hollywood-2). We also compare the performances of the proposed
isolated and continuous recognition algorithms with several recently published
methods
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