2 research outputs found
Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model
Accurate prediction of traffic signal duration for roadway junction is a
challenging problem due to the dynamic nature of traffic flows. Though
supervised learning can be used, parameters may vary across roadway junctions.
In this paper, we present a computer vision guided expert system that can learn
the departure rate of a given traffic junction modeled using traditional
queuing theory. First, we temporally group the optical flow of the moving
vehicles using Dirichlet Process Mixture Model (DPMM). These groups are
referred to as tracklets or temporal clusters. Tracklet features are then used
to learn the dynamic behavior of a traffic junction, especially during on/off
cycles of a signal. The proposed queuing theory based approach can predict the
signal open duration for the next cycle with higher accuracy when compared with
other popular features used for tracking. The hypothesis has been verified on
two publicly available video datasets. The results reveal that the DPMM based
features are better than existing tracking frameworks to estimate . Thus,
signal duration prediction is more accurate when tested on these datasets.The
method can be used for designing intelligent operator-independent traffic
control systems for roadway junctions at cities and highways
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras
Aerators are essential and crucial auxiliary devices in intensive culture,
especially in industrial culture in China. The traditional methods cannot
accurately detect abnormal condition of aerators in time. Surveillance cameras
are widely used as visual perception modules of the Internet of Things, and
then using these widely existing surveillance cameras to realize real-time
anomaly detection of aerators is a cost-free and easy-to-promote method.
However, it is difficult to develop such an expert system due to some technical
and applied challenges, e.g., illumination, occlusion, complex background, etc.
To tackle these aforementioned challenges, we propose a real-time expert system
based on computer vision technology and existing surveillance cameras for
anomaly detection of aerators, which consists of two modules, i.e., object
region detection and working state detection. First, it is difficult to detect
the working state for some small object regions in whole images, and the time
complexity of global feature comparison is also high, so we present an object
region detection method based on the region proposal idea. Moreover, we propose
a novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm
for motion feature extraction in fixed regions. Then, we present a dimension
reduction method of time series for establishing a feature dataset with obvious
boundaries between classes. Finally, we use machine learning algorithms to
build the feature classifier. The experimental results in both the actual video
dataset and the augmented video dataset show that the accuracy for detecting
object region and working state of aerators is 100% and 99.9% respectively, and
the detection speed is 77-333 frames per second (FPS) according to the
different types of surveillance cameras.Comment: 17 figure