Fast Vehicle Detection with Probabilistic Feature Grouping and Its Application to Vehicle Tracking
and
Automated Railroad Crossing Violation Detection

ZuWhan Kim


First Part

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 algorithms. I present the system implementation and the vehicle detection and tracking results.

Second Part

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.


Maintained by Philippos Mordohai