Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wildlife Conservation

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images Dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIDIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management.