Fast Vehicle Detection with Probabilistic Feature Grouping and Its
Application to Vehicle Tracking
Automated Railroad Crossing Violation Detection
Generating vehicle trajectories from video data is an important
application of ITS (Intelligent Transportation Systems).
I introduce a new tracking approach which uses model-based 3-D vehicle
detection and description algorithm.
Our vehicle detection and description algorithm is based on
a probabilistic line feature grouping, and it is faster (by up to
an order of magnitude) and more flexible than previous image-based
I present the system implementation and the vehicle detection and
It is important to understand the factors underlying grade
crossing crashes, and to examine potential solutions.
We have installed a camera in front of a locomotive
to examine grade crossing accidents (or near accidents).
We present a computer vision system that automatically extracts
possible near accident scenes by detecting activity of
vehicles crossing in front of the train after signals are ignited.
We present a fast algorithm to detecting moving objects
recorded by a moving camera with minimal computation.
The moving object is detected by 1) estimating ego-motion of the camera,
and 2) detecting and tracking feature points whose motion is
inconsistent with the camera motion.
We introduce a pseudo-realtime ego-motion (camera motion) estimation method
with a robust optimization algorithm.
We present experiments on ego-motion estimation and moving object detection.
Our algorithm works in pseudo-realtime and we expect that our algorithm
can be applied to realtime applications such as collision warning
in the near future with the development of hardware technology.