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Computer-vision system for predatory wildlife identification

ML pipeline replaces manual expert review of camera-trap footage with automated species detection.

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CASE FILE · CS-05 SHIPPED

“We went from sampling footage to processing all of it. The conservation insight is now data-driven, not anecdotal.”

Camera-trap footage now processed automaticallyTB/mo

ClientWildlife conservation organization

SectorSports

Service linesBuild · Agents

Window8 weeks fixed

READ THE FILE ↓

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)

Wildlife in natural habitat — CASE FILE · CS-05 · COMPUTER-VISION SYSTEM FOR PREDATORY WILDLIFE IDENTIFICATION

ML pipeline replaces manual expert review of camera-trap footage with automated species detection.

ENGAGEMENT TIMELINE · 8 WEEKS FIXED

Every engagement runs through the same five gates of the FORGE method. Here’s how this case ran.

W0 · FRAME

Species classification rubric, edge-case dataset review (lighting / occlusion / partial-frame), labelled-data audit.

W1 · OUTLINE

Pipeline architecture, Sagemaker training plan, feature-extraction design (fur patterns, facial features, body shape), batch + real-time modes.

W2–5 · REBUILD

Preprocessing layer (lighting normalisation, frame stabilisation), model ensemble training, cloud inference pipeline.

W6 · GOVERN

Confidence-threshold tuning, human-review queue for low-confidence frames, model-card documentation.

W7–8 · ENGAGE

First-month deployment across camera-trap network, feedback loop on misclassifications, retraining cadence agreed.

RESULTS · KEY METRICS

Automated

Species detection from raw camera-trap footage

Scalable

Cloud-native inference handles TB/month throughput

Confidence-scored

Low-confidence frames routed to human review

Self-improving

Continuous-learning loop from human-flagged corrections

STACK · CS-05SHIPPED

SectorSports

ServicesBuild · Agents

ClientWildlife conservation organization (anonymized)

Python PyTorch / TensorFlow OpenCV AWS Sagemaker AWS Lambda S3 Custom feature-extraction pipeline

Client voice

We went from sampling footage to processing all of it. The conservation insight is now data-driven, not anecdotal.

Conservation Director · wildlife organization

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