Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.