Comparative Analysis Of Various Opencv Methods For Automatic Species Detection Using Camera Trap Images

Abstract

Biodiversity monitoring and conservation is essential for protecting ecosystem and to ensure the ecological resilience to disturbances including climatic change, disease outbreaks, and human exploitation. Camera traps is a vital tool for wildlife species monitoring that generate vast collections of images which require automated processing.  Numerous approaches are available in literature. Though deep learning techniques provide higher accuracy in automated species detection, many conservation projects still employs traditional image processing techniques using OpenCV due to its lightweight nature and inherent limitation to computational resources. This paper presents the first comprehensive, head‑to‑head evaluation of six OpenCV based species detection techniques including three classical methods (Background Subtraction - Contour Analysis, Haar‑Cascade, HOG + SVM)  and three deep learning based methods (MobileNet‑SSD, YOLOv5‑ONNX, EfficientDet‑D0) across three publicly available camera trap datasets (Snapshot Serengeti, Caltech Camera Traps and CamTrapAsia). Results show that YOLOv5‑ONNX achieves the highest mean Average Precision (mAP = 93.4%). The classical Background Contour method still remains effective for large species (elephants) (F1 = 0.79) while running  faster on Raspberry Pi 4 hardware. This study highlights a trade‑offs in accuracy, inference speed, energy footprint, and data requirements, providing actionable guidelines for biologists in selecting OpenCV pipelines under real ‑ world conditions.