Wild Vision
Sponsored Third Year B.Tech Project
Python · Machine Learning · Computer Vision
A machine learning-based wildlife monitoring system developed as a sponsored academic project. Explores computer vision pipelines, real-time detection, and applied AI models for ecological intelligence.
Problem
Wildlife monitoring at scale requires continuous observation across large geographic areas — a task that is both labour-intensive and error-prone when handled manually. Existing solutions lack real-time detection capability and struggle with the variability of natural environments.
Approach
Constructed a computer vision pipeline trained on wildlife imagery to detect and classify species in real time. The system was designed around low-latency inference, environmental robustness, and a modular architecture that allows model components to be updated independently of the data pipeline.
Tools
Built in Python using standard ML libraries for model training and inference. The detection pipeline uses a convolutional architecture fine-tuned on domain-specific data. Data processing and annotation workflows were scripted for reproducibility across training runs.
Outcome
A functional detection system validated against a curated wildlife dataset. The project was assessed and sponsored at the third-year B.Tech level, demonstrating real-time inference capability across test footage. The architecture is designed to be extended with additional species classes and sensor inputs.