457 research outputs found
The automatic detection of patterns in people's movements
Bibliography: leaves 102-105
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
We present a global optimization approach to optical flow estimation. The
approach optimizes a classical optical flow objective over the full space of
mappings between discrete grids. No descriptor matching is used. The highly
regular structure of the space of mappings enables optimizations that reduce
the computational complexity of the algorithm's inner loop from quadratic to
linear and support efficient matching of tens of thousands of nodes to tens of
thousands of displacements. We show that one-shot global optimization of a
classical Horn-Schunck-type objective over regular grids at a single resolution
is sufficient to initialize continuous interpolation and achieve
state-of-the-art performance on challenging modern benchmarks.Comment: To be presented at CVPR 201
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
DeepFlow: Large displacement optical flow with deep matching
International audienceOptical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed DeepFlow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. DeepFlow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset
DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute
dense correspondences between images. DeepMatching relies on a hierarchical,
multi-layer, correlational architecture designed for matching images and was
inspired by deep convolutional approaches. The proposed matching algorithm can
handle non-rigid deformations and repetitive textures and efficiently
determines dense correspondences in the presence of significant changes between
images. We evaluate the performance of DeepMatching, in comparison with
state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al
2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013)
datasets. DeepMatching outperforms the state-of-the-art algorithms and shows
excellent results in particular for repetitive textures.We also propose a
method for estimating optical flow, called DeepFlow, by integrating
DeepMatching in the large displacement optical flow (LDOF) approach of Brox and
Malik (2011). Compared to existing matching algorithms, additional robustness
to large displacements and complex motion is obtained thanks to our matching
approach. DeepFlow obtains competitive performance on public benchmarks for
optical flow estimation
Speaker independent isolated word recognition
The work presented in this thesis concerns the recognition of
isolated words using a pattern matching approach. In such a system,
an unknown speech utterance, which is to be identified, is
transformed into a pattern of characteristic features. These
features are then compared with a set of pre-stored reference
patterns that were generated from the vocabulary words. The unknown
word is identified as that vocabulary word for which the reference
pattern gives the best match.
One of the major difficul ties in the pattern comparison process is
that speech patterns, obtained from the same word, exhibit non-linear
temporal fluctuations and thus a high degree of redundancy. The
initial part of this thesis considers various dynamic time warping
techniques used for normalizing the temporal differences between
speech patterns. Redundancy removal methods are also considered, and
their effect on the recognition accuracy is assessed.
Although the use of dynamic time warping algorithms provide
considerable improvement in the accuracy of isolated word recognition
schemes, the performance is ultimately limited by their poor ability
to discriminate between acoustically similar words. Methods for
enhancing the identification rate among acoustically similar words,
by using common pattern features for similar sounding regions, are
investigated.
Pattern matching based, speaker independent systems, can only operate
with a high recognition rate, by using multiple reference patterns
for each of the words included in the vocabulary. These patterns are
obtained from the utterances of a group of speakers. The use of
multiple reference patterns, not only leads to a large increase in
the memory requirements of the recognizer, but also an increase in
the computational load. A recognition system is proposed in this
thesis, which overcomes these difficulties by (i) employing vector
quantization techniques to reduce the storage of reference patterns,
and (ii) eliminating the need for dynamic time warping which reduces
the computational complexity of the system.
Finally, a method of identifying the acoustic structure of an
utterance in terms of voiced, unvoiced, and silence segments by using
fuzzy set theory is proposed. The acoustic structure is then
employed to enhance the recognition accuracy of a conventional
isolated word recognizer
On robust optical flow estimation on image sequences with differently exposed frames using primal-dual optimization
Optical flow methods are used to estimate pixelwise motion information based on consecutive frames in image sequences. The image sequences traditionally contain frames that are similarly exposed. However, many real-world scenes contain high dynamic range content that cannot be captured well with a single exposure setting. Such scenes result in certain image regions being over- or underexposed, which can negatively impact the quality of motion estimates in those regions. Motivated by this, we propose to capture high dynamic range scenes using different exposure settings every other frame. A framework for OF estimation on such image sequences is presented, that can straightforwardly integrate techniques from the state-of-the-art in conventional OF methods. Different aspects of robustness of OF methods are discussed, including estimation of large displacements and robustness to natural illumination changes that occur between the frames, and we demonstrate experimentally how to handle such challenging flow estimation scenarios. The flow estimation is formulated as an optimization problem whose solution is obtained using an efficient primal–dual method
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