Abstract:
Animal farms have been steadily growing to meet the consumption requirements of the society in an e cient manner. This fact necessitates new monitoring and tracking systems to collect useful information about the herds in order to observe their general health and instantaneous state. However, recognizing and tracking an animal in a farm is a di cult task due to the target's similarity and hard to predict dynamics. In this thesis, a novel cow identi cation system is proposed. There are prominent features of this solution which di erentiates it from the others in the literature, i.e., it does not need any markers or external devices placed on the animal; works in even unlighted environments; identi es even black cows without distinctive coat patterns; is relatively cheaper, and enables accurate positioning. Proposed solution is based on 3D shape analysis of the top back part of the animals captured with RGBD cameras placed at an adequate height, where two dimensional images are constructed with respect to the local surface features and are subsequently identi ed by using face recognition methods. To evaluate the applicability of the proposed system, a real-time prototype software has been developed and a 3D cattle dataset is acquired which, to our knowledge, is unique in the literature. This dataset is gathered from moving animals which do not have distinctive coat patterns and captured in di erent lighting conditions. Applicability of the proposed solution has been veri ed by testing with the acquired dataset. Convincing results are obtained where %88 of 50 cows are identi ed successfully.
