We introduce 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.

Search optimization algorithms have the challenge of balancing between exploration of the search space (e.g., map locations, image pixels) and exploitation of learned information (e.g., prior knowledge, regions of high fitness). To address this challenge, we present a very basic framework which we call Zombie Survival Optimization (ZSO), a novel swarm intelligence ap- proach modeled after the foraging behavior of zombies. Zombies (exploration agents) search in a space where the underlying fitness is modeled as a hypothetical air- borne antidote which cures a zombie’s aliments and turns them back into humans (who attempt to survive by exploiting the search space). Such an optimization al- gorithm is useful for search, such as searching an image for a pedestrian. Experiments on the CAVIAR dataset suggest improved efficiency over Particle Swarm Op- timization (PSO) and Bacterial Foraging Optimization (BFO). A C++ implementation is available.

Pedestrian tracking is an important problem with many practical applications in fields such as security, animation, and human computer interaction (HCI). We introduce a previously-unexplored swarm intelligence approach to multi-object monocular tracking by using Bacterial Foraging Optimization (BFO) swarms to drive a novel part-based pedestrian appearance tracker. We show that tracking a pedestrian by segmenting the body into parts outperforms popular blobbased methods and that using BFO can improve performance over traditional Particle Swarm Optimization and Particle Filter methods.

A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. This paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level) is analyzed both in simulation and in real-life experiments and compared with previous approaches.

One of the key problems in computer vision and pattern recognition is tracking. Multiple objects, occlusion, and tracking moving objects using a moving camera are some of the challenges that one may face in developing an ef- fective approach for tracking. While there are numerous algorithms and approaches to the tracking problem with their own shortcomings, a less-studied approach considers swarm intelligence. Swarm intelligence algorithms are often suited for optimization problems, but require advancements for tracking objects in video. We present an improved algorithm based on Bacterial Foraging Optimization in order to track multiple objects in real-time video exposed to full and partial occlusion, using video from both ¯xed and moving cameras. A comparison with various algorithms is provided.