Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the lfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition,  propose a system for recognizing body parts of dogs kept in pens.  decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.
['Simone Pistocchi', 'Simone Calderara', 'Shanis Barnard', 'Nicola Ferri', 'Rita Cucchiara']
I am writing a research paper on a idea Kernelized Structural Classification for 3D Dogs Body Parts Detection
[{"from": "human", "value": "I am writing a research paper on a idea Kernelized Structural Classification for 3D Dogs Body Parts Detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the lfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition,  propose a system for recognizing body parts of dogs kept in pens.  decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy."}]