700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ ReadersThis Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Research Article Open Access
Object tracking is an important task within the field of computer vision. The high-powered computers, the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms. In this paper, we are proposing a new object tracking algorithm that employs a swarm-intelligence based method, particle swarm optimization (PSO).Firstly, all potential solutions are projected into a high-dimensional space where particles are initialized. Then, particles are driven by PSO rules to search for the solutions. The object is tracked when the particles reach convergence. In this paper, we are going to track the Table Tennis Ball, as Ball tracking is very challenging task because of the ball speed, illumination changes, and overlapping of the object. We are using color as feature to uniquely identify the ball and calculate the fitness function, which are used in PSO to drive the particles towards the ball.
Object, Swarm, Particle, Fitness Function, gBest, pBest, PSO, Artificial Intelligence Studies