TrackEverything — Model-Agnostic Tracking Layer

Problem

Single-frame detection and classification systems often produce unstable results across video. Predictions can flicker, miss objects, or ignore useful temporal evidence.

Constraints

  • Should work with models from different Python ML libraries
  • Should not require rewriting the underlying detector or classifier
  • Should use information gathered across multiple frames
  • Should support practical experimentation with tracking algorithms

Approach

Built a Python package that can take detection or classification models from libraries such as TensorFlow or PyTorch and add tracking logic around them.

The system uses statistical evidence collected across frames to improve consistency and make better use of temporal information.

Result

Created a reusable model-agnostic tracking layer for improving detection/classification workflows in video contexts.

Commercial relevance

This case is relevant to teams building video AI, object tracking, inspection systems, safety monitoring, robotics perception, or any computer-vision workflow where single-frame predictions are too noisy.

Confidentiality note

This is a public technical project. Performance depends on the underlying model, data, and tracking configuration.