Master’s Project Proposal Briefing Bill Champlin Java Quasi-Connected Components (JQCC) Tracking System March 10, 2009 Advisor - Dr. Terrance Boult

LOTS Background [1][2] LOTS was developed for tracking human motion in omnidirectional images, such as for sniper tracking Has been and continues to be adapted to other domains such as tracking of navel ships and UAVs Has been rehosted to various architectures and can optionally utilize MMX libraries for increased performance Image taken from [1] 2

LOTS Background – Con’t Employs a technique called Quasi-Connected Components (QCC) – Given target pixels above threshold, connects additional pixels to targets that are below threshold but above background and close in proximity to target pixels – Puts more pixels on target – increasing probability of detection – Allows for a higher threshold setting which reduces false alarms caused by background clutter 3

LOTS Software Current baseline is around 96 KSLOC of C++ – Numerous conditional compile statements to support various architectures i.e. Intel MMX and different input cameras Drives large size somewhat as core functionality is replicated – Other non core functionality provided also contributes to size, such as image flattening to reduce convex mirror distortion Undocumented and sparsely commented 4

Objectives Gain a general understanding of LOTS software and algorithm techniques including: backgrounding, thresholding, pixel labeling, clustering & centroiding, and tracking across time – Activities: Obtain s/w baseline and study current functionality Study existing source code and extract core functionality (QCC) Develop requirements specification and design Port LOTS core functionality to Java (JQCC), includes: – Creating front end interface to input image movies – Displaying processed images and ROI tracks – Generation of output tracking reports Perform testing and develop certification specification – Verify execution under Windows and Linux platforms 5

Objectives – Con’t Learn how to adapt LOTS algorithm techniques to a new domain area – Activities: Modify and tune JQCC to track objects in the night sky, such as satellites, the International Space Station (ISS), airplanes, meteors, etc. Obtain image sequences – either via internet (i.e. NASA’s JPL) or capture with a camera and home telescope Compare JQCC performance – Activities: JQCC vs. LOTS (is pure Java fast enough?) Compare and analyze results Optimize as necessary OPTIONAL ACTIVITY TIME PERMITTING: Compare JQCC vs. another publicly available tracking algorithm 6

Tasks Mid Feb – March 31 – Establish executable baseline – Study functionality – Strip out LOTS core functionality April 1 - April 18 – Develop requirements specification April 19 – May 31 – Translate and re-host core functionality in Java 7

Tasks – Con’t June 1 – June 30 – Modify & tune for night sky images July 1 – July 31 – Analyze JQCC performance August 1 – August 21 – Publish project report – Publish JQCC to the web – Publish all deliverables to UCCS Grad Studies repository November – Project defense 8

Deliverables Per UCCS C.S. Guidelines: – Requirements spec – Certification spec – User’s handbook – Source code (including test drivers, images and outputs) Short, informal project report (report is mandatory for research projects, but not software development efforts) – Summarize re-hosting activities – UML design – Performance – Analysis of adapting it to tracking night sky objects – Optionally, the results of comparison with an alternate tracking algorithm Bi-weekly status reports to Dr. Boult (and any other committee members if requested) All products uploaded to UCCS Grad Studies repository and posted on the web 9

References [1] T.E. Boult, R.J. Micheals, X. Gao, M. Eckmann, “Into the woods: visual surveillance of non-cooperative and camouflaged targets in complex outdoor settings”, in Proc. Of the IEEE, Oct [2] T.E. Boult, T. Zhang, R.C. Johnson, “Two threshold are better than one”, CVPR, pp.1-8, 2007 IEEE Conference on Computer Vision and Pattern Recognition 10