49,979 research outputs found
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Video Registration in Egocentric Vision under Day and Night Illumination Changes
With the spread of wearable devices and head mounted cameras, a wide range of
application requiring precise user localization is now possible. In this paper
we propose to treat the problem of obtaining the user position with respect to
a known environment as a video registration problem. Video registration, i.e.
the task of aligning an input video sequence to a pre-built 3D model, relies on
a matching process of local keypoints extracted on the query sequence to a 3D
point cloud. The overall registration performance is strictly tied to the
actual quality of this 2D-3D matching, and can degrade if environmental
conditions such as steep changes in lighting like the ones between day and
night occur. To effectively register an egocentric video sequence under these
conditions, we propose to tackle the source of the problem: the matching
process. To overcome the shortcomings of standard matching techniques, we
introduce a novel embedding space that allows us to obtain robust matches by
jointly taking into account local descriptors, their spatial arrangement and
their temporal robustness. The proposal is evaluated using unconstrained
egocentric video sequences both in terms of matching quality and resulting
registration performance using different 3D models of historical landmarks. The
results show that the proposed method can outperform state of the art
registration algorithms, in particular when dealing with the challenges of
night and day sequences
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
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
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