000150397 001__ 150397
000150397 005__ 20250203171510.0
000150397 0247_ $$2doi$$a10.1109/IROS45743.2020.9341600
000150397 0248_ $$2sideral$$a142506
000150397 037__ $$aART-2020-142506
000150397 041__ $$aeng
000150397 100__ $$aSabater, Alberto$$uUniversidad de Zaragoza
000150397 245__ $$aRobust and efficient post-processing for video object detection
000150397 260__ $$c2020
000150397 5203_ $$aObject recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing methods by introducing a learning-based similarity evaluation between detections across frames. Our method improves the results of stat-of-the-art specific video detectors, specially regarding fast moving objects, and presents low resource requirements. And applied to efficient still image detectors, such as YOLO, provides comparable results to much more computationally intensive detectors.
000150397 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17R$$9info:eu-repo/grantAgreement/ES/MCIU-AEI/RTC-2017-6421-7$$9info:eu-repo/grantAgreement/ES/MICIU-AEI-FEDER/PGC2018-098817-A-I00
000150397 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000150397 592__ $$a0.596$$b2020
000150397 593__ $$aComputer Science Applications$$c2020
000150397 593__ $$aSoftware$$c2020
000150397 593__ $$aControl and Systems Engineering$$c2020
000150397 593__ $$aComputer Vision and Pattern Recognition$$c2020
000150397 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150397 700__ $$0(orcid)0000-0003-1183-349X$$aMontesano, Luis$$uUniversidad de Zaragoza
000150397 700__ $$0(orcid)0000-0002-7580-9037$$aMurillo, Ana C.$$uUniversidad de Zaragoza
000150397 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000150397 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000150397 773__ $$g(2020), 10536-10542$$pProc. IEEE/RSJ Int. Conf. Intell. Rob. Syst.$$tProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems$$x2153-0858
000150397 8564_ $$s2473992$$uhttps://zaguan.unizar.es/record/150397/files/texto_completo.pdf$$yVersión publicada
000150397 8564_ $$s3249760$$uhttps://zaguan.unizar.es/record/150397/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150397 909CO $$ooai:zaguan.unizar.es:150397$$particulos$$pdriver
000150397 951__ $$a2025-02-03-14:50:15
000150397 980__ $$aARTICLE