Autonomous herding research is becoming increasingly relevant. In this work, a model for sheep detection in herds from aerial video sequences is proposed, using the convolutional neural network Mask R-CNN. Several trainings with different datasets have been performed for achieving the model. An improvement in the detection metrics, through a visual tracking tool, allows not only detecting the individual sheeps in the herd, but also tracking them along the different frames in aerial video sequences. This system could be used, for example, in a drone to carry out livestock supervision, in addition to obtaining metrics that allow knowing the status of the herd. Finally, the method has been validated using several tests on images and videos of livestock in real outdoor environments.


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    Title :

    Detection and Tracking of Livestock Herds from Aerial Video Sequences


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Publication date :

    2022-11-19


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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