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High Dynamic Range Video Analysis for Video Surveillance

giuseppevalenzise – Posted on April 11, 2017 at 7:26 am –

Many high-level computer vision algorithms such
as object and face recognition, tracking, classification, etc., are extremely
sensitive to changes in the appearance of a scene produced by drastic lighting variations. These
occur often in practical applications, e.g., day/night changes in outdoor
recognition tasks; stark shadows created by objects in the scene; varying
meteorological and geographical conditions in video surveillance applications
employing drones; specular reflections caused by specific materials in the
scene; and so on.
 Not surprisingly,
coping with extreme lighting changes has attracted a lot of attention in the
computer vision community in the past few years, where traditional approaches
such as local feature extraction have been refined and empowered by a massive
use of machine learning techniques. However, these methods systematically fail
when their input lacks sufficient information, e.g., due to acquisition noise
or saturation resulting in under-exposed or over-exposed areas of the video.

In this PhD thesis, we consider an alternative
and promising approach, based on using High
Dynamic Range (HDR) video
for computer vision tasks. Indeed, HDR has the
potential to overcome the instability due to lighting changes, as it can
capture simultaneously details in both very bright and very dark regions and
visually reproduce the whole range of luminance present in the original scene.
 The goal of the Ph.D. thesis
is to study and test novel tools for the HDR video processing chain, when this
is oriented to perform security-related computer vision tasks. Specifically,
the Ph.D. candidate will explore task-optimized tone mapping operators for both
compression and pre-processing of HDR video that will be analyzed by a computer
vision algorithm. A special attention will be given to recent techniques like
DNNs (deep neural networks) and GANs (generative adversarial networks) to
design new video-processing pipelines. This Ph.D. research will be conducted at
the Laboratory of Signals and Systems
(L2S), CNRS, CentraleSupelec, Université Paris-Sud, France,
collaboration with the WMG, University
of Warwick, UK
. In particular, the candidate will build on initial work done
in the two labs on visual feature extraction and tone mapping design. In
addition, he/she will get access to specialized hardware such as an HDR
display, novel HDR video systems and photometric probes. This research is
expected to have a large impact on security related computer vision
applications, and to significantly improve state-of-the-art performance in the
detection of events or menaces in challenging environments, as well as for
recognition and identification systems.

Keywords: High Dynamic Range, video
surveillance, machine learning

Profile and skills required

We look for a candidate with European
or Swiss nationality
, having obtained a master or an equivalent diploma in
engineering or applied math. The ideal candidate has an excellent record of results
obtained during his/her studies, as well as solid bases in signal and image
processing, machine learning and/or computer vision. A strong expertise with
programming languages such as C/C++, Python and Matlab is required, as well as proficiency
in English (both written and spoken). Knowledge of French is a plus.

Post description

The candidate will have a 3 year Ph.D. contract at L2S lab, France, and
will have the opportunity to spend short research stays in the WMG partner lab,

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