Abstract: In this paper we present a new method for over-height
vehicle detection in low headroom streets and highways using digital
video possessing. The accuracy and the lower price comparing to
present detectors like laser radars and the capability of providing
extra information like speed and height measurement make this
method more reliable and efficient. In this algorithm the features are
selected and tracked using KLT algorithm. A blob extraction
algorithm is also applied using background estimation and
subtraction. Then the world coordinates of features that are inside the
blobs are estimated using a noble calibration method. As, the heights
of the features are calculated, we apply a threshold to select overheight
features and eliminate others. The over-height features are
segmented using some association criteria and grouped using an
undirected graph. Then they are tracked through sequential frames.
The obtained groups refer to over-height vehicles in a scene.
Abstract: This paper presents an effective traffic lights
recognition method at the daytime. First, Potential Traffic Lights
Detector (PTLD) use whole color source of YCbCr channel image and
make each binary image of green and red traffic lights. After PTLD
step, Shape Filter (SF) use to remove noise such as traffic sign, street
tree, vehicle, and building. At this time, noise removal properties
consist of information of blobs of binary image; length, area, area of
boundary box, etc. Finally, after an intermediate association step witch
goal is to define relevant candidates region from the previously
detected traffic lights, Adaptive Multi-class Classifier (AMC) is
executed. The classification method uses Haar-like feature and
Adaboost algorithm. For simulation, we are implemented through Intel
Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and
rural roads. Through the test, we are compared with our method and
standard object-recognition learning processes and proved that it
reached up to 94 % of detection rate which is better than the results
achieved with cascade classifiers. Computation time of our proposed
method is 15 ms.
Abstract: With the increase of economic behavior and the upgrade
of living standar, the ratio for people in Taiwan who own automobiles
and motorcycles have recently increased with multiples. Therefore,
parking issues will be a big challenge to facilitate traffic network and
ensure urban life quality. The Parking Guidance and Information
System is one of important systems for Advanced Traveler Information
Services (ATIS). This research proposes a parking guidance and
information system which integrates GPS and 3G network for a map on
the Geographic Information System to solution inadequate of roadside
information kanban. The system proposed in this study mainly includes
Parking Host, Parking Guidance and Information Server, Geographic
Map and Information System as well as Parking Guidance and
Information Browser. The study results show this system can increase
driver-s efficiency to find parking space and efficiently enhance
parking convenience in comparison with roadside kanban system.
Abstract: Urban road network traffic has become one of the
most studied research topics in the last decades. This is mainly due to
the enlargement of the cities and the growing number of motor
vehicles traveling in this road network. One of the most sensitive
problems is to verify if the network is congestion-free. Another
related problem is the automatic reconfiguration of the network
without building new roads to alleviate congestions. These problems
require an accurate model of the traffic to determine the steady state
of the system. An alternative is to simulate the traffic to see if there
are congestions and when and where they occur. One key issue is to
find an adequate model for road intersections. Once the model
established, either a large scale model is built or the intersection is
represented by its performance measures and simulation for analysis.
In both cases, it is important to seek the queueing model to represent
the road intersection. In this paper, we propose to model the road
intersection as a BCMP queueing network and we compare this
analytical model against a simulation model for validation.