Multi-Object Tracking with Transformer Neural Networks on Range-Doppler Maps
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Updated
May 17, 2025 - Python
Multi-Object Tracking with Transformer Neural Networks on Range-Doppler Maps
Real-time radar perception pipeline for automotive systems with multi-target detection, velocity estimation, and spatial localization.
Reverse engineering and experimental tooling for the Seeed Studio MR60BHA2 / ADT6101P 60 GHz FMCW radar: coherent range-Doppler streams, guarded firmware patching, and ESP32-C6 web visualization.
Decode mmWave radar and synchronized multi-sensor sessions or live streams into Python training and real-time inference pipelines backed by Rust.
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