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|Title: ||Agent-based framework for person re-identification|
|Authors: ||Al-Rahbi, Muna S.|
|Issue Date: ||2017|
|Publisher: ||© Muna Saif Al-Rahbi|
|Abstract: ||In computer based human object re-identification, a detected human is recognised to a
level sufficient to re-identify a tracked person in either a different camera capturing the
same individual, often at a different angle, or the same camera at a different time and/or
the person approaching the camera at a different angle. Instead of relying on face
recognition technology such systems study the clothing of the individuals being monitored
and/or objects being carried to establish correspondence and hence re-identify the human
Unfortunately present human-object re-identification systems consider the entire human
object as one connected region in making the decisions about similarity of two objects
being matched. This assumption has a major drawback in that when a person is partially
occluded, a part of the occluding foreground will be picked up and used in matching. Our
research revealed that when a human observer carries out a manual human-object re-identification
task, the attention is often taken over by some parts of the human
figure/body, more than the others, e.g. face, brightly colour shirt, presence of texture
patterns in clothing etc., and occluding parts are ignored.
In this thesis, a novel multi-agent based framework is proposed for the design of a human
object re-identification system. Initially a HOG based feature extraction is used in a SVM
based classification of a human object as a human of a full-body or of half body nature.
Subsequently the relative visual significance of the top and the bottom parts of the human,
in re-identification is quantified by the analysis of Gray Level Co-occurrence based
texture features and colour histograms obtained in the HSV colour space. Accordingly
different weights are assigned to the top and bottom of the human body using a novel
probabilistic approach. The weights are then used to modify the Hybrid Spatiogram and
Covariance Descriptor (HSCD) feature based re-identification algorithm adopted.
A significant novelty of the human object re-identification systems proposed in this thesis
is the agent based design procedure adopted that separates the use of computer vision
algorithms for feature extraction, comparison etc., from the decision making process of re-identification. Multiple agents are assigned to execute different algorithmic tasks and
the agents communicate to make the required logical decisions.
Detailed experimental results are provided to prove that the proposed multi agent based
framework for human object re-identification performs significantly better than the state of-the-art algorithms. Further it is shown that the design flexibilities and scalabilities of
the proposed system allows it to be effectively utilised in more complex computer vision
based video analytic/forensic tasks often conducted within distributed, multi-camera
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Computer Science)|
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