Boxmot is a multi-object tracking framework designed to follow multiple objects across video frames using motion and appearance algorithms to maintain consistent identities. It functions as a system for tracking objects with specific orientations using rotated bounding boxes and corresponding intersection-over-union computations.
The project includes a re-identification model optimizer that converts neural networks into formats for hardware-accelerated execution. It also features an evolutionary hyperparameter tuner that iteratively mutates tracker settings to maximize accuracy for specific datasets.
The framework provides capabilities for computer vision benchmarking, including the use of persistent detection caching to speed up evaluation cycles. Additional functionality includes the ability to integrate tracking logic into native compiled applications via build tools.
