In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG & Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods.

    Design/methodology/approach

    This paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking.

    Findings

    Better tracking algorithms are not mentioned in the existing method.

    Research limitations/implications

    Visual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success.

    Practical implications

    The authors implement the multiple tracking methods, for better tracking purpose.

    Originality/value

    The main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.


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    Title :

    An experimental study on visual tracking based on deep learning


    Additional title:

    Visual tracking based deep learning


    Contributors:


    Publication date :

    2021-12-20


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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