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Collision detection for visual tracking of crane loads using a particle filter

This paper presents a novel method for detecting collisions of a swinging crane load using a particle filter for visual tracking. The particle filter uses a dynamical model of the nominal motion of the crane load, and an alternative dynamical model that describes the crane load under collision. A jump Markov model is formulated with dynamics described by one of the models. The particle filter detects whether the object motion is due to the nominal model or the alternative model. The particle filter tracks markers placed on the crane load. The performance of the proposed method is demonstrated in laboratory experiments.