This is the source code for my MSc project, titled "Omnidirectional Visual Tracking", that was submitted in 2003 for my MSc Engineering & Information Science degree at the University of Reading.
The source code is C++. The dataset used for training and testing the algorithms is the PETS 2001 dataset that is available for download at http://www.cvg.reading.ac.uk/slides/pets.html. Use the version that is acquired using the catadioptric sensor.
A catadioptric sensor is a camera made up of a mirror (catoptrics) and lenses (dioptrics). By simplifying things a lot, one can think of a catadioptric sensor as consisting of a normal camera viewing the world reflected in a parabolic-shaped mirror. To be useful, the mirror must be perfectly aligned with and placed at a precise distace from the camera. The main advantage of a catadioptric sensor is its panoramic view of the world, being able to view a full hemisphere (360 degrees by 90 degrees. Some example images of catadioptric sensors and the output that they give can be found here: http://www.cis.upenn.edu/~kostas/omni.html.
Below is the reproduced abstract of my thesis.
ABSTRACT
Omnidirectional vision is the ability to see in all directions at the same time. Sensors that are able to achieve omnidirectional vision offer several advantages to many areas of computer vision, such as that of tracking and surveillance. This area benefits from the unobstructed views of the surroundings acquired by these sensors and allows objects to be tracked simultaneously in different parts of the field-of-view without requiring any camera motion.
This thesis describes a system that uses an omnidirectional camera system for the purpose of detecting and tracking moving objects. We describe the methods used for detecting objects, by means of a motion detection technique, and investigate two different methods for tracking objects – a method based on tracking groups of moving pixels, commonly referred to as blob tracking methods, and a statistical colour-based method that uses a mixture model for representing the object’s colours. We evaluate these methods and show the robustness of the latter method for occlusion and other problems that are normally encountered in tracking applications.
For this thesis, we make use of a catadioptric omnidirectional camera based on a paraboloidal mirror, because of its single viewpoint and its flexibility of calibration and use. We also describe the methods used to generate virtual perspective views from the non-linear omnidirectional images acquired by the catadioptric camera. This is used in combination with the tracking results to create virtual cameras that produce perspective video streams while automatically tracking objects as they move within the camera’s field-of-view.
Finally, this thesis demonstrates the advantage that an omnidirectional visual tracking system provides over limited field-of-view systems. Objects have the potential of being tracked for as long as they remain in the scene, and are not lost because they exit the field-of-view as happens for the latter systems. This should ultimately lead to a better awareness of the surrounding world – of the objects present in the scene and their behaviour.