Visual object tracking using particle clustering

Computer vision been used to estimate object location relatively from observer on many applications. High definition sensor often used to gain accuracy of the object tracking which resulting high processing complexity. Lower resolution sensor simplifies the process with significant accuracy lost. Particle clustering method estimates the object location by grouping several detection data with certain similarity. Instead of detecting edges and corner on the visual data, this paper uses clustering method to group pixels with certain similarity and measure its element. The cluster measured both height and width to estimate the distance of the object from the observer. New color features introduced in this research promising a better detection approach even with low resolution sensor. The proposed approach successfully provides 30fps image analysis with significant color extraction improvement.