A Hybrid Approach for Understanding Animal Behavior Using Deep Learning Techniques

Animals need health and wellbeing monitoring. It requires constant care, and observation. Manual tracking involves humans visually observing and recording animal movements which makes data management difficult and time-consuming. This automated animal behavior tracking system uses machine learning to accurately observe and comprehend animal behavior. The suggested study uses VGG-16 and ResNet50 for animal identification and LRCN for behavior detection. ResNet50 and VGG-16 process camera tape data. ResNet50 outperforms VGG-16 in cat and dog identification. The LRCN model helps identify animal behavior accurately. The UCF-50 dataset was first created to predict general human behavior, but it did not specifically reflect the actions of cats and dogs, which limited its application to these animals. In order to close this gap and make sure the LRCN model could successfully adapt to cats and dogs, for this study, a customized dataset centered on their distinctive behavioral pattern was exclusively created. The problem of reducing false positives in behavior detection is also addressed by the hybrid strategy that combines LRCN with ResNet50, producing predictions that are more accurate and trustworthy. Proactive health and behavior monitoring is encouraged by this integration, which improves the system's capacity to recognize, detect, and forecast animal behaviors.