In order to solve the problem that the KCF tracking algo-rithm has occlusion or deformation and the disturbance fac-tors such as similar objects cause tracking failure; this paper proposes an improved algorithm combining VGG-16 neural network. Firstly, the VGG-16 network's powerful feature ex-traction capability is used to extract features that are more ro-bust to deformation and occlusion from different layers and different operations. Then, using the cyclic shift matrix of KCF algorithm, a large number of sample training classifiers are generated, and then new images are calculated. The filter-ing response of the block predicts the target position; in order to improve the real-time performance of the algorithm, the model and the new strategy for the KCF algorithm reduce the computational complexity by updating the model with a fixed frame interval. Compared with the traditional KCF algorithm, this method can effectively deal with the interference factors such as deformation and occlusion, and can achieve target tracking more quickly while ensuring accuracy.


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    Title :

    KCF TRACKING ALGORITHM BASED ON VGG16 DEPTH FRAMEWORK


    Contributors:
    Zhang, Sida (author) / Li, Wei (author) / Wang, Renfang (author)

    Publication date :

    2019-04-25


    Remarks:

    International Journal of Advanced Computer Technology; Vol 8 No 2 (2019): IJACT Journal; 05-09 ; 2319-7900


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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