Ci Li
Automatic horse lameness detection through 2D to 3D reconstruction
Abstract
Lameness is the fatal disease of horses and veterinaries often make a diag-
nosis based on their experience. In this thesis, we investigate whether neural
networks can do lameness detection of horses by using the 3D reconstructed
model of the horses. We divide the problem into two parts. The first part is
about the 3D model reconstruction of the horse in the videos and then we use
neural networks to do lameness detection. We also perform experiments on
human videos to test the generalization of our idea, reconstructing the 3D hu-
man model in the videos and doing action recognition with neural networks.
The two frameworks we use are standard LSTM and LSTM with an attention
mechanism. The results of the human experiments show that both networks
can separate human actions given the 3D human model sequences and some
specific joints are pointed out when doing the two-class action classification.
The results of animal experiments preliminarily show that the information of
the 3D horse model can be used to perform lameness detection and front-limb
lameness is more comfortable for the networks to learn compared to hind-limb
lameness.
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