Moving Object Tracking in Occluded and Cluttered Backgrounds Using Adaptive Kalman Filtering

Ahmed, Mohammed A.; Ahn, Youngshin; Choi, Joon Weon

doi:10.1007/978-81-322-0997-3_49

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the different kinds of tracking methods, the Mean Shift tracking algorithm [2,3] due to its efficiency and simplicity, was used in many tracking contexts. However, in the classical Mean Shift algorithm, an estimation of scale and orientation changes of the target is not involved.…”

Section: Introductionmentioning

confidence: 99%

Scale and Orientation Adaptive Moving Object Tracking in a Sequence of Imageries

Ren

Choi

Ahmed

et al. 2013

AMR

Self Cite

View full text Add to dashboard Cite

Object tracking has been researched for many years as an important topic in machine learning, robot vision and many other fields. Over the years, various tracking methods are proposed and developed in order to gain a better tracking effect. Among them the mean-shift algorithm turns out to be robust and accurate compared other algorithms after different kinds of tests. But due to its limitations, the changes in scale and rotational motion of an object cannot be effectively processed. This problem occurs when the object of interest moves towards or away from the video camera. Improving over the previously proposed method such as scale and orientation adaptive mean shift tracking, which performs well with scaling change but not for the rotation, in this paper, the proposed method modifies the continuously adaptive mean shift tracking method so that it can handle effectively for changes in size and rotation in motion, simultaneously. The simulation results yield a successful tracking of moving objects even when the object undergoes scaling in size and rotation in motion in comparison to the conventional ones.

show abstract

Section: Introductionmentioning

confidence: 99%