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Dual approach for object tracking based on optical flow and swarm intelligence
In Computer Vision, object tracking is a very old and complex problem. Though there are several existing algorithms for object tracking, still there are several challenges remain to be solved.
A Modification of Particle Swarm Optimization using Random Walk
We are proposing a novel approach where particles movement will not be depend on its velocity rather it will be decided by constrained biased random walk of particles.
Object Tracking based on Quantum Particle Swarm Optimization
In Computer Vision domain, moving Object Tracking considered as one of the toughest problem. As there so many factors associated like illumination of light, noise, occlusion, sudden start and stop of moving object, shading which makes tracking even harder problem not only for dynamic background but also for static background. In this paper we present a new object tracking algorithm based on Dominant points on tracked object using Quantum particle swarm optimization (QPSO) which is a new different version of PSO based on Quantum theory.
