Abstract: Surveillance system is widely used in the traffic
monitoring. The deployment of cameras is moving toward a
ubiquitous camera (UbiCam) environment. In our previous study, a
novel service, called GPS-VT, was firstly proposed by incorporating
global positioning system (GPS) and visual tracking techniques for
the UbiCam environment. The first prototype is called GODTA
(GPS-based Moving Object Detection and Tracking Approach). For a
moving person carried GPS-enabled mobile device, he can be
tracking when he enters the field-of-view (FOV) of a camera
according to his real-time GPS coordinate. In this paper, GPS-VT
service is applied to the tracking of vehicles. The moving speed of a
vehicle is much faster than a person. It means that the time passing
through the FOV is much shorter than that of a person. Besides, the
update interval of GPS coordinate is once per second, it is
asynchronous with the frame rate of the real-time image. The above
asynchronous is worsen by the network transmission delay. These
factors are the main challenging to fulfill GPS-VT service on a
vehicle.In order to overcome the influence of the above factors, a
back-propagation neural network (BPNN) is used to predict the
possible lane before the vehicle enters the FOV of a camera. Then, a
template matching technique is used for the visual tracking of a target
vehicle. The experimental result shows that the target vehicle can be
located and tracking successfully. The success location rate of the
implemented prototype is higher than that of the previous GODTA.
Abstract: This paper proposes an alternative control mechanism
for an interactive Pan/Tilt/Zoom (PTZ) camera control system.
Instead of using a mouse or a joystick, the proposed mechanism
utilizes a Nintendo Wii remote and infrared (IR) sensor bar. The Wii
remote has buttons that allows the user to control the movement of a
PTZ camera through Bluetooth connectivity. In addition, the Wii
remote has a built-in motion sensor that allows the user to give
control signals to the PTZ camera through pitch and roll movement.
A stationary IR sensor bar, placed at some distance away opposite the
Wii remote, enables the detection of yaw movement. In addition, the
Wii remote-s built-in IR camera has the ability to detect its spatial
position, and thus generates a control signal when the user moves the
Wii remote. Some experiments are carried out and their performances
are compared with an industry-standard PTZ joystick.
Abstract: Current image-based individual human recognition
methods, such as fingerprints, face, or iris biometric modalities
generally require a cooperative subject, views from certain aspects,
and physical contact or close proximity. These methods cannot
reliably recognize non-cooperating individuals at a distance in the
real world under changing environmental conditions. Gait, which
concerns recognizing individuals by the way they walk, is a relatively
new biometric without these disadvantages. The inherent gait
characteristic of an individual makes it irreplaceable and useful in
visual surveillance.
In this paper, an efficient gait recognition system for human
identification by extracting two features namely width vector of
the binary silhouette and the MPEG-7-based region-based shape
descriptors is proposed. In the proposed method, foreground objects
i.e., human and other moving objects are extracted by estimating
background information by a Gaussian Mixture Model (GMM) and
subsequently, median filtering operation is performed for removing
noises in the background subtracted image. A moving target classification
algorithm is used to separate human being (i.e., pedestrian)
from other foreground objects (viz., vehicles). Shape and boundary
information is used in the moving target classification algorithm.
Subsequently, width vector of the outer contour of binary silhouette
and the MPEG-7 Angular Radial Transform coefficients are taken as
the feature vector. Next, the Principal Component Analysis (PCA)
is applied to the selected feature vector to reduce its dimensionality.
These extracted feature vectors are used to train an Hidden Markov
Model (HMM) for identification of some individuals. The proposed
system is evaluated using some gait sequences and the experimental
results show the efficacy of the proposed algorithm.