Dog Breed Identification using ResNet Model

As dogs are domestic animals due to the many numbers of dog breeds available around the world. It’s hard to find out the exact dog breed name for a common person. There are many techniques available to identify dog breed. But the proposed work introduced the new technique called RESNET which is the part of CNN to classify dog. RESNET is used to identify images. It helps to perform different tasks on larger datasets. Identification of different dogs is one of the important applications of Convolutional Neural networks. Since the identification of dog breeds is very difficult because they spread in a large number and it makes very hard for a person to identify or classify dogs. With the help of Keras and TensorFlow, a dataset is created, tested, and trained for the detection of dog breeds by using RESNET. Around 120 different dog breeds are present in the dataset which consist of 20600 images of dogs. From this paper, load these images and convert them into a NumPy array and normalize them. Then,100 epochs were used with a batch size of 128 to achieve the best accuracy. The model is saved for further process to create a web application to identify the dog

An Intelligent Dog Breed Recognition System Using Deep Learning

Image processing has been getting great attention recently in the field of machine learning and deep learning. This technique can be used to process an image in such a way that the computer understands the features of the image and classifies it. Our study focuses on building an efficient CNN model to predict the breed of the dog using its image, giving the best accuracy possible with the least amount of computing resources involved. This CNN model is deployed on cloud service, Google App Engine which identifies certain characteristics or features in an image such as the paw, nose, stout, and ears of a dog, employing a dataset containing 10222 images of different dog breeds or classes of dogs and opening a wide scope for future developments

Cat Breeds Classification Using Convolutional Neural Network For Multi-Object Image

Cat is one of the most popular pets. There are many cat breeds with unique characteristic and treatment for each breed. A cat owner can have more than one cat, either the same breed or different breeds.  But not all cat owners know the breeds of their cats. Computers can be trained to recognized cat breeds, but there are many challenges for computers because it limited by how much they have been trained and programmed. In recent years, a lot of research about image classification has been done before and got various result, but most of the data used in previous research were single object images. Therefore, this study of cat breeds classification would be conducted with Convolutional Neural Network (CNN) in the Multi-Object images. This method was chosen because it had good classification results in the previous studies. This study used 5 breeds of cats with every breed having 200-3200 images for training. The test results were measured using confusion matrix, obtaining the precision, recall, f1 score and accuracy of 100% on multi-object images with 2 objects and 3 objects. On images with 4 objects achieved the precision, recall, f1 score and accuracy value of 89%, 87%, 87% and 95%. While the value of precision, recall, f1 score and accuracy on images with 5 objects get 87%, 86%, 86% and 94%, respectively

Dog Identification using Soft Biometrics and Neural Networks

This paper addresses the problem of biometric identification of animals, specifically dogs. We apply advanced machine learning models such as deep neural network on the photographs of pets in order to determine the pet identity. In this paper, we explore the possibility of using different types of "soft" biometrics, such as breed, height, or gender, in fusion with "hard" biometrics such as photographs of the pet's face. We apply the principle of transfer learning on different Convolutional Neural Networks, in order to create a network designed specifically for breed classification. The proposed network is able to achieve an accuracy of 90.80% and 91.29% when differentiating between the two dog breeds, for two different datasets. Without the use of "soft" biometrics, the identification rate of dogs is 78.09% but by using a decision network to incorporate "soft" biometrics, the identification rate can achieve an accuracy of 84.94%

Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors

Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a “behaviour tracker”: a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations

Dog face detection using yolo network

This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92. © 2020, Brno University of Technology. All rights reserved

ARTIFICIAL REALITY INTERACTION MODELS

In some implementations, the technology can render a dense layout of interactive mechanisms (e.g., selectable text and/or graphics) that are responsive to low accuracy input methods on the XR device. In some implementations, an XR device can associate a shortcut with the physical object (e.g., an action relative to the physical object, an option to perform an action relative to the physical object, etc.). In some implementations, a workload manager can select an augmentation workload using one or more of captured environment data (e.g., captured visual frames, audio, etc.), output from the initial stage model(s), any other suitable data