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Co-design system for template matching using dedicated co-processor and particle swarm optimization

Template matching is an important technique used for object tracking. It aims at finding a given pattern within a frame sequence. Pearson's Correlation Coefficient (PCC) is widely used to quantify the degree of similarity of two images. This coefficient is computed for each image pixel. This entails a computationally very expensive process. In this paper, aiming at accelerating this process, we propose to implement the template matching as an embedded co-design system. In order to reduce the processing time, a dedicated co-processor, which is responsible of performing the PCC computation is designed and implemented. Particle Swarm Optimization (PSO) is used to improve the search for the maximum correlation point of the image and the used template. The search process is implemented in software and is run by an embedded general purpose processor. The performance results show that the designed system achieves real-time requirements as needed in real-word applications.