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An Improved CAMShift Algorithm for Abdulmalik Danlami Mohammed* and Tim Morris School of Computer Science University of Manchester Oxford Road, Manchester, UK abdulmalik.mohammed@postgrad.manchester.ac.uk,
tim.morris@manchester.ac.uk
Continuously Adaptive MeanShift
(CAMShift) is an important algorithm for object tracking based on the colour
histogram. The algorithm works by finding the mode of a probability
distribution map within a search window and iteratively updates the position
and size of the window until convergence. The algorithm boasts of high
performance in a simple environment where the colour distribution is constant.
However, because the algorithm is dependent on a static colour distribution,
its performance suffers in cases where the distribution changes e.g. due to
illumination or weather conditions. In addition, object occlusion and complex
background colour can degrade the performance of the algorithm. In this
paper, we propose a CAMShift algorithm that can track coloured signs. Since multiple
colours are involved for tracking, we utilized a Bayesian approach to
estimate the colour probability density function. This probability density
function gives the probability of whether a pixel value corresponds to certain
object. We illustrate the effectiveness of our approach by detecting and
extracting visual sign images with different colour attributes. The result
obtained shows that our extended CAMShift algorithm can detect and track
coloured signs based on the identified colour class. Keywords: CAMShift algorithm, object tracking, colour histogram *Corresponding Author: Abdulmalik Danlami Mohammed 2.94 Kilburn Building, Oxford Road. M13 9PL, Manchester, United
Kingdom, Tel:+447741052107 IT
Convergence Practice (INPRA), Vol. 2, No. 2, pp. 43-54, June 2014 [pdf] |