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Stellenbosch University (2015)

Detection of black-backed jackal in still images

Pathare, Sneha P.

Titre : Detection of black-backed jackal in still images

Auteur : Pathare, Sneha P.

Université de soutenance : Stellenbosch University

Grade : Master of Science (MS) 2015

Résumé
In South Africa, black-back jackal (BBJ) predation of sheep causes heavy losses to sheep farmers. Different control measures such as shooting, gin-traps and poisoning have been used to control the jackal population ; however, these techniques also kill many harmless animals, as they fail to differentiate between BBJ and harmless animals. In this project, a system is implemented to detect black-backed jackal faces in images. The system was implemented using the Viola-Jones object detection algorithm. This algorithm was originally developed to detect human faces, but can also be used to detect a variety of other objects. The three important key features of the Viola-Jones algorithm are the representation of an image as a so-called ”integral image”, the use of the Adaboost boosting algorithm for feature selection, and the use of a cascade of classifiers to reduce false alarms. In this project, Python code has been developed to extract the Haar-features from BBJ images by acting as a classifier to distinguish between a BBJ and the background. Furthermore, the feature selection is done using the Asymboost instead of the Adaboost algorithm so as to achieve a high detection rate and low false positive rate. A cascade of strong classifiers is trained using a cascade learning algorithm. The inclusion of a special fifth feature Haar feature, adapted to the relative spacing of the jackal’s eyes, improves accuracy further. The final system detects 78% of the jackal faces, while only 0.006% of other image frames are wrongly identified as faces.

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Page publiée le 3 février 2016, mise à jour le 30 mars 2020