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Preformer MOT: Un enfoque basado en transformadores para el seguimiento de múltiples objetos con predicción de
Yueying Wang1, Yuhao Qing1, Kaer Huang2
1School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, China.
Fundamental research
|February 6, 2026
Resumen
Preformer MOT mejora el seguimiento de múltiples objetos prediciendo trayectorias futuras de objetos mediante un enfoque basado en transformadores. Este método mejora la precisión en entornos dinámicos y escenarios complejos.
Área de la Ciencia:
- Visión por Computadora
- Inteligencia Artificial
- Robótica
Sus antecedentes:
- Los métodos de seguimiento de múltiples objetos (MOT) a menudo tienen dificultades para predecir las trayectorias futuras de los objetos.
- Los enfoques existentes subutilizan la información temporal para una predicción de trayectorias robusta.
- Los desafíos incluyen oclusiones, objetos superpuestos y movimiento no lineal.
Objetivo del estudio:
- Introducir Preformer MOT, un método novedoso basado en transformadores para la predicción precisa de trayectorias no lineales.
- Mejorar el seguimiento de múltiples objetos incorporando información de fotogramas futuros.
- Abordar la subutilización de las correlaciones temporales en MOT.
Principales métodos:
- Utiliza una arquitectura basada en transformadores (Preformer MOT).
- Combina una técnica novedosa de estimación de movimiento con predicción de trayectorias y filtrado de Kalman.
- Emplea un modelo de predicción de trayectorias autosupervisado para la estimación de la posición futura.
- Incorpora la predicción de múltiples fotogramas para manejar interrupciones en la trayectoria.
Principales resultados:
- Preformer MOT mejora significativamente la precisión de las predicciones de trayectorias no lineales.
- El método demuestra un rendimiento superior en conjuntos de datos de peatones (DanceTrack, MOT17).
- Logra resultados excepcionales en entornos marinos complejos, mostrando adaptabilidad.
Conclusiones:
- Preformer MOT ofrece una solución integral para el seguimiento de múltiples objetos con amplias correlaciones temporales.
- El enfoque predice eficazmente las trayectorias futuras, mejorando la continuidad durante las interrupciones.
- Demuestra una amplia aplicabilidad en entornos diversos y desafiantes.
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