The aim of the research study is to address the numerous challenges associated with developing a productive approach for monitoring, categorizing, and identifying animals. Many computational models that can precisely recognize and track animals in various surroundings must be developed in order to create such a system.To this end, numerous techniques have been proposed and evaluated to improve the efficiency and accuracy of animal segmentation, detection, classification, and tracking.In particular, the proposed techniques have focused on separating animals from their surroundings, which is a critical step in the accurate categorization of animals.Two approaches were suggested to complete this job, and the effectiveness of the suggested animal segmentation algorithm was assessed using a region-based performance metric. The proposed classification model utilized a variety of features and classifiers to accurately categorize animals. Using segmented animal pictures, Gabor, color, and LBP were extracted, and the potential for combining the features to enhance classification performance was investigated.Furthermore, extracted features were represented in the form of interval-valued type data to preserve inter and intra-class variations of animals. The classification of animals was accomplished using symbolic classifiers and SVM, which allowed for the accurate categorization of animals based on their features. Finally, a model was proposed to segment, track, and label animals in videos, which utilized a region merging-based segmentation algorithm and the nearest neighbor classifier. This model was divided into three stages: segmentation, tracking, and labeling. In the first stage, an animal video was given as input, and frames were extracted from the videos. Segmentation was performed for all edges using a region merging-based segmentation algorithm. In the second stage, video frames were used to track the animal in entire animal videos, and Gabor features were extracted from the images and stored in the knowledgebase for labeling. The classification of animals was accomplished using the nearest neighbor classifier.Overall, the techniques and models proposed in this research have the potential to significantly improve the accuracy and efficiency of animal detection, classification, and tracking systems, thereby facilitating research in numerous fields, such as wildlife conservation, animal behavior research, and more.

Animal recognition system, LBPH, PCA, SVM, and neural networks.