A Novel Enhanced Algorithm for Efficient Human Tracking

Mehdi Gheisari, Zohreh Safari, Mohammad Almasi, Amir Hossein Pourishaban Najafabadi, Ragesh G K, Yang Liu, Aaqif Afzaal Abbasi, Seyed Mojtaba Hosseini Bamakan


Tracking moving objects have been an issue in recent years of computer vision and Image processing. And human tracking makes it a more significant challenge. This category has various aspects and wide applications, such as autonomous deriving, human-robot interactions, and human movement analysis. One of the issues that have always made tracking algorithms difficult is their interaction with goal recognition methods, the mutable appearance of variable aims, and simultaneous tracking of multiple goals. In this paper, a method with high efficiency and higher accuracy compared to the previous methods for tracking just objects using imaging with the fixed camera is introduced. The proposed algorithm operates in four steps in such a way to identify a fixed background and removing noise from that. This background is used to subtract from movable objects. After that, while the image is being filtered, the shadows and noises of the filmed image are removed, and finally, using the bubble routing method, the mobile object will be separated and tracked. Experimental results indicate that the proposed model for detecting and tracking mobile objects works well and can improve the motion and trajectory estimation of objects in terms of speed and accuracy to a desirable level up to %10 in terms of accuracy compared with previous methods.


Object Tracking; Movable Objects; Image Filter; Background Subtraction; Bubble Routing; Human tracking; Human detection; Deep learning.


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DOI: http://doi.org/10.11591/ijict.v11i1.pp%25p


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