Najib BEN AOUN
Najib BEN AOUN received the Bachelor degree in 2002 in experimental sciences and the Four-year degree in computer sciences in 2006 from the Higher Institute in Applied Sciences and Technologies of Sousse (ISSATS), Tunisia. He received the master degree in the New Technologies of the Dedicated Computer Systems (NTSID) in 2008 from the National School of Engineers of Sfax (ENIS), Tunisia. He is preparing his Ph.D in the Research Group in Intelligent Machines (REGIM) at the same School (ENIS). He is currently an IEEE Student member, SPS Member and the chairman of the IEEE SPS Student Branch Chapter at ENIS during 2011 (treasurer during 2010). He served as a chairman session in the 10th International Conference in Signal Processing (ICSP’2010) and a reviewer for the IEEE Conference on Computer Applications & Industrial Electronics (ICCAIE’2011). He is also a reviewer for the 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP’2012). His main interests include video processing and analysis, computer vision, video event detection, multimedia retrieval and detection, pattern recognition and video compression and encoding.
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Books by Najib BEN AOUN
the video data. That is why a big interest has been made for developing an efficient video
coding system and improving the motion estimation part which represents the most
important part since it consumes most computation time and most resources used for video
coding. Making the motion estimation a fast and efficient process was the goal of many
researchers. But, unfortunately, that was not reached in the spatial domain. That’s why, new
ME systems have been conducted in other domain such as the frequency and the
multiresolution domain. That is why many studies have been made to improve and simplify
the ME methods. In this chapter, we have studied the wavelet as a domain for ME and we
have proposed a multiresolution motion estimation and compensation method based on
block matching applying in the wavelet coefficients. Because of some problems presented in
this chapter, we have integrated some improvements techniques to ameliorate our ME
system. As a future works, we will reinforce our method with others techniques such as the
spatial segmentation which makes the estimation more accurate by trying to identify real
objects in the predicted moving zones.
Papers by Najib BEN AOUN
evolution especially with the emergence of many video
applications over networks such as the videophone and the
videoconferencing, and multimedia devices such as the highdefinition
TV and the personal digital assistants. So, it was
crucial to reduce the quantity of data stored or transmitted by
compressing it spatially and temporally. Hence, motion
estimation and compensation are employed in video coding
systems to remove temporal redundancy while keeping a high
visual quality. They are the most important parts of the video
coding process since they require the most computational power
and the biggest consumption in resources and bandwidth.
Therefore, many techniques have been developed to estimate
motion between successive frames. In this paper, we will present
our motion estimation and compensation method applied on the
discrete wavelet transform coefficients and based on the block
matching algorithm which is the simplest, the most efficient and
the most popular technique. Additional techniques are
introduced to accelerate the estimation process and improve the
prediction quality."
textual descriptions for many of them, video annotation became a highly
desired task. Conventional systems try to annotate a video query by simply
finding its most similar videos in the database. Although the video annotation
problem has been tackled in the last decade, no attention has been paid to the
problem of assembling video keyframes in a sensed way to provide an answer
of the given video query when no single candidate video turns out to be similar
to the query. In this paper, we introduce a graph based image modeling and
indexing system for video annotation. Our system is able to improve the video
annotation task by assembling a set of graphs representing different keyframes
of different videos, to compose the video query. The experimental results
demonstrate the effectiveness of our system to annotate videos that are not
possibly annotated by classical approaches.
field in research and industry. Information detection or retrieval
were a challenged task especially with the spread of multimedia
applications and the increased number of the video acquisition
devices such as the surveillance cameras, phones cameras. These
have produced a large amount of video data which are also
diversified and complex. This is what makes event detection in
video a difficult task. Many video event detection methods were
developed which are composed of two fundamental parts: video
indexing and video classification. In this paper, we will introduce
a new video event detection system based on graphs. Our system
models the video frame as a graph in addition to a motion
description. Thereafter, these models were classified and events
are detected. Experimental results proved the effectiveness and
the robustness of our system.
for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection.We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in the context
of feature extraction, events learning and detection in large collection of video surveillance dataset.