access icon free Improved appearance updating method in multiple instance learning tracking

Multiple instance learning (MIL) tracker becomes recently very popular because of their great success in complex scenes. Dynamically reflecting the appearance changes of the tracked object, the appearance updating plays an important role on tracking. In the original MIL tracker, the appearance model is assumed to obey normal distribution and its updating rule consists of a simple linearly weighted sum of the original and the current target distributions in the current frame. However, this updating method is not proved theoretically. In this work, the authors deduce a novel appearance updating method by estimating the mean and the variance of the sum of two normal distributions being merged in maximum likelihood estimation. The method can be naturally extended to multivariable distributions, useful to track colour object. Experimental results on some benchmark video sequences show that the method achieve higher precision and reliability than the three state-of-art trackers.

Inspec keywords: maximum likelihood estimation; normal distribution; image sequences; image colour analysis; object tracking; learning (artificial intelligence)

Other keywords: maximum likelihood estimation; video sequences; linearly weighted sum; MIL tracker; normal distribution; multivariable distributions; updating rule; colour object tracking; appearance model; improved appearance updating method; multiple instance learning tracking

Subjects: Knowledge engineering techniques; Video signal processing; Computer vision and image processing techniques; Other topics in statistics; Other topics in statistics; Optical, image and video signal processing

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