(1) the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result, Real-Time Pedestrian Tracking with Bacterial Foraging Optimization (2) the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper, Hoang Thanh Nguyen Winston Chung Hall Room 216 University of California, Riverside Riverside, CA 92521 nthoang@cs.ucr.edu 1-951-907-0527 Bir Bhanu Winston Chung Hall Room 216 University of California, Riverside Riverside, CA 92521 bhanu@ee.ucr.edu 1-951-827-3954 (3) the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition), Hoang Thanh Nguyen (4) the abstract of the paper(s), In this paper, we present swarm intelligence algorithms for pedestrian tracking. In particular, we present a modified Bacterial Foraging Optimization (BFO) algorithm and show that it outperforms PSO in a number of important metrics for pedestrian tracking. In our experiments, we show that BFO’s search strategy is inherently more efficient than PSO under a range of variables with regard to the number of fitness evaluations which need to be performed when tracking. We also compare the proposed BFO approach with other commonly-used trackers and present experimental results on the CAVIAR dataset as well as on the difficult PETS2010 S2.L3 crowd video. (5) a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies, B (6) a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission), Pedestrian tracking is a challenging problem in which evolutionary algorithms have recently been applied. The Modified Bacterial Foraging Optimization (m-BFO) algorithm in the paper results in tracking performance which is better than previous state-of-the-art tracking algorithms [1] on the CAVIAR tracking dataset [2]. (7) a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); H.T. Nguyen and B. Bhanu. Real-Time Pedestrian Tracking with Bacterial Foraging Optimization. IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2012. (8) a statement either that “any prize money, if any, is to be divided equally among the co-authors” OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; and Any prize money, if any, is to be divided equally among the co-authors. (9) a statement stating why the judges should consider the entry as “best” in comparison to other entries that may also be “human-competitive.” This entry should be considered as best in comparison to other entries because it helps facilitate real-time tracking systems with an algorithm which improves both accuracy and speed over traditional approaches. [1] N. Seo. OpenCVX: Yet another OpenCV eXtension. http://code.google.com/p/opencvx/. [2] R. B. Fisher. The PETS04 surveillance ground-truth datasets. In IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2004. http://homepages.inf.ed.ac.uk/rbf/CAVIAR.