2,112 research outputs found
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a βshotβ based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was βbroadcastβ based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
Reliable camera motion estimation from compressed MPEG videos using machine learning approach
As an important feature in characterizing video content, camera motion has been widely applied in various multimedia and computer vision applications. A novel method for fast and reliable estimation of camera motion from MPEG videos is proposed, using support vector machine for estimation in a regression model trained on a synthesized sequence. Experiments conducted on real sequences show that the proposed method yields much improved results in estimating camera motions while the difficulty in selecting valid macroblocks and motion vectors is skipped
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Automatic object classification for surveillance videos.
PhDThe recent popularity of surveillance video systems, specially located in urban
scenarios, demands the development of visual techniques for monitoring purposes.
A primary step towards intelligent surveillance video systems consists on automatic
object classification, which still remains an open research problem and the keystone
for the development of more specific applications.
Typically, object representation is based on the inherent visual features. However,
psychological studies have demonstrated that human beings can routinely categorise
objects according to their behaviour. The existing gap in the understanding
between the features automatically extracted by a computer, such as appearance-based
features, and the concepts unconsciously perceived by human beings but
unattainable for machines, or the behaviour features, is most commonly known
as semantic gap. Consequently, this thesis proposes to narrow the semantic gap
and bring together machine and human understanding towards object classification.
Thus, a Surveillance Media Management is proposed to automatically detect and
classify objects by analysing the physical properties inherent in their appearance
(machine understanding) and the behaviour patterns which require a higher level of
understanding (human understanding). Finally, a probabilistic multimodal fusion
algorithm bridges the gap performing an automatic classification considering both
machine and human understanding.
The performance of the proposed Surveillance Media Management framework
has been thoroughly evaluated on outdoor surveillance datasets. The experiments
conducted demonstrated that the combination of machine and human understanding
substantially enhanced the object classification performance. Finally, the inclusion
of human reasoning and understanding provides the essential information to bridge
the semantic gap towards smart surveillance video systems
A review of content-based video retrieval techniques for person identification
The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
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