Real-Time Pedestrian Tracking with Bacterial Foraging Optimization

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.