5,574 research outputs found
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
Driver drowsiness detection in facial images
Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, drowsy
driver alert systems are meant to reduce the main cause of traffic accidents. Different
approaches have been developed to tackle with the fatigue detection problem. Though
most reliable techniques to asses fatigue involve the use of physical sensors to monitor
drivers, they can be too intrusive and are less likely to be adopted by the car industry. A
relatively new and effective trend consists on facial image analysis from video cameras
that monitor drivers.
How to extract effective features of fatigue from images is important for many image
processing applications. This project proposes a face descriptor that can be used to detect
driver fatigue in static frames. This descriptor represents each frame of a sequence as
a pyramid of scaled images that are divided into non-overlapping blocks of equal size.
The pyramid of images is combined with three different image descriptors. The final
descriptors are filtered out using feature selection and a Support Vector Machine is used
to predict the drowsiness state. The proposed method is tested on the public NTHUDDD
dataset, which is the state-of-the-art dataset on driver drowsiness detection
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