A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets

We propose a novel particle swarm optimisation algorithm that uses a set of interactive swarms to track multiple pedestrians in a crowd. The proposed method improves the standard particle swarm optimisation algorithm with a dynamic social interaction model that enhances the interaction among swarms. In addition, we integrate constraints provided by temporal continuity and strength of person detections in the framework. This allows particle swarm optimisation to be able to track multiple moving targets in a complex scene. Experimental results demonstrate that the proposed method robustly tracks multiple targets despite the complex interactions among targets that lead to several occlusions.

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► Introduces an idea of multiple interactive swarms to track moving targets in a crowd.
► Incorporates higher level information in the process of finding optima in a high dimensional space.
► Integrates constraints provided by social behaviour of crowd, temporal continuity and the strength of pedestrian detections.
► Is able to track unknown and varying number of targets in a crowded environment.
► Comparison with state-of-the-art methods shows the improved performance.