Computer-vision system for predatory wildlife identification

Challenge

Hundreds of camera-trap units generated TB of footage per month. Manual expert review was slow, expensive, and inconsistent. Visual similarity between predator species (and between predators and large herbivores) made naive image classification unreliable. Lighting, weather, occlusion, and partial-frame captures all degraded model accuracy. The team needed a production-grade ML pipeline that could run scalably on cloud infrastructure and self-improve over time.

Solution

A computer-vision pipeline with custom preprocessing (lighting normalization, frame stabilization, occlusion handling), feature extraction tuned for fur patterns / facial features / body shape, and an ensemble of classification models trained on labeled species datasets. Built as a scalable AWS-based inference pipeline with batch + real-time modes. Confidence-scored outputs flagged low-confidence frames for human review, building a continuous-improvement loop.

Engagement

• Sector: Sports
• Service lines: Build · Agents
• Client: Wildlife conservation organization (anonymized)