10,404 research outputs found
Navigation domain representation for interactive multiview imaging
Enabling users to interactively navigate through different viewpoints of a
static scene is a new interesting functionality in 3D streaming systems. While
it opens exciting perspectives towards rich multimedia applications, it
requires the design of novel representations and coding techniques in order to
solve the new challenges imposed by interactive navigation. Interactivity
clearly brings new design constraints: the encoder is unaware of the exact
decoding process, while the decoder has to reconstruct information from
incomplete subsets of data since the server can generally not transmit images
for all possible viewpoints due to resource constrains. In this paper, we
propose a novel multiview data representation that permits to satisfy bandwidth
and storage constraints in an interactive multiview streaming system. In
particular, we partition the multiview navigation domain into segments, each of
which is described by a reference image and some auxiliary information. The
auxiliary information enables the client to recreate any viewpoint in the
navigation segment via view synthesis. The decoder is then able to navigate
freely in the segment without further data request to the server; it requests
additional data only when it moves to a different segment. We discuss the
benefits of this novel representation in interactive navigation systems and
further propose a method to optimize the partitioning of the navigation domain
into independent segments, under bandwidth and storage constraints.
Experimental results confirm the potential of the proposed representation;
namely, our system leads to similar compression performance as classical
inter-view coding, while it provides the high level of flexibility that is
required for interactive streaming. Hence, our new framework represents a
promising solution for 3D data representation in novel interactive multimedia
services
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for
sparse, time-varying signals using side information. Our first method utilizes
extra cross-validation measurements, and the second one exploits extra
low-resolution measurements. Unlike the majority of current CS techniques, we
do not assume that we know an upper bound on the number of significant
coefficients that comprise the images in the video sequence. Instead, we use
the side information to predict the number of significant coefficients in the
signal at the next time instant. For each image in the video sequence, our
techniques specify a fixed number of spatially-multiplexed CS measurements to
acquire, and adjust this quantity from image to image. Our strategies are
developed in the specific context of background subtraction for surveillance
video, and we experimentally validate the proposed methods on real video
sequences
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Depth image based rendering techniques for multiview applications have been
recently introduced for efficient view generation at arbitrary camera
positions. Encoding rate control has thus to consider both texture and depth
data. Due to different structures of depth and texture images and their
different roles on the rendered views, distributing the available bit budget
between them however requires a careful analysis. Information loss due to
texture coding affects the value of pixels in synthesized views while errors in
depth information lead to shift in objects or unexpected patterns at their
boundaries. In this paper, we address the problem of efficient bit allocation
between textures and depth data of multiview video sequences. We adopt a
rate-distortion framework based on a simplified model of depth and texture
images. Our model preserves the main features of depth and texture images.
Unlike most recent solutions, our method permits to avoid rendering at encoding
time for distortion estimation so that the encoding complexity is not
augmented. In addition to this, our model is independent of the underlying
inpainting method that is used at decoder. Experiments confirm our theoretical
results and the efficiency of our rate allocation strategy
Depth map compression via 3D region-based representation
In 3D video, view synthesis is used to create new virtual views between
encoded camera views. Errors in the coding of the depth maps introduce
geometry inconsistencies in synthesized views. In this paper, a new 3D plane
representation of the scene is presented which improves the performance of
current standard video codecs in the view synthesis domain. Two image segmentation
algorithms are proposed for generating a color and depth segmentation.
Using both partitions, depth maps are segmented into regions without
sharp discontinuities without having to explicitly signal all depth edges. The
resulting regions are represented using a planar model in the 3D world scene.
This 3D representation allows an efficient encoding while preserving the 3D
characteristics of the scene. The 3D planes open up the possibility to code
multiview images with a unique representation.Postprint (author's final draft
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