Visual Object Detection for Animal Behavior Research

The main contribution of this thesis is the development and implementation of object recognition framework for zebras. The proposed image descriptors are particularly easy to understand, and provide an example of how simple yet effective specialized features can be devised for a given object category. In general, designing image descriptors is a challenging task due to a fact that categorizing objects in images is an inherently ambiguous process, with image formation process, compression, intra-class variation and occlusion all contributing to ambiguity on different levels. However, we can take advantage of some domain-specific features in order to provide compact and efficient image descriptors. According to experimental results, our recog- nition framework achieves comparable performance to the state of the art techniques generally used in computer vision, while maintaining very fast training times, and easily parallelizable classification implementation.