Robust and efficient post-processing for video object detection

Sabater, Alberto (Universidad de Zaragoza) ; Montesano, Luis (Universidad de Zaragoza) ; Murillo, Ana C. (Universidad de Zaragoza)

Resumen: Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing methods by introducing a learning-based similarity evaluation between detections across frames. Our method improves the results of stat-of-the-art specific video detectors, specially regarding fast moving objects, and presents low resource requirements. And applied to efficient still image detectors, such as YOLO, provides comparable results to much more computationally intensive detectors.

Idioma: Inglés

DOI: 10.1109/IROS45743.2020.9341600

Año: 2020

Publicado en: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems (2020), 10536-10542
ISSN: 2153-0858

Factor impacto SCIMAGO: 0.596 - Computer Science Applications - Software - Control and Systems Engineering - Computer Vision and Pattern Recognition

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17R
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI/RTC-2017-6421-7
Financiación: info:eu-repo/grantAgreement/ES/MICIU-AEI-FEDER/PGC2018-098817-A-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)