Robust Pedestrian Detection: Faster Deployments with Fusion of Models

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Pedestrian detection has a wide range of real-world critical applications including security and management of emergency scenarios. In critical applications, detection recall and precision are both essential to ensure the correct detection of all pedestrians. The development and deployment of object detection vision-based models is a time-consuming task, depending on long training and fine-tuning processes to achieve top performance. We propose an alternative approach, based on a fusion of pre-trained off-the-shelf state-of-the-art object detection models, and exploit base model divergences to quickly deploy robust ensembles with improved performance. Our approach promotes model reuse and does not require additional learning algorithms, making it suitable for rapid deployments of critical systems. Experimental results, conducted on PASCAL VOC07 test dataset, reveal mean average precision (mAP) improvements over base detection models, regardless of the set of models selected. Improvements in mAP were observed starting from just two detection models and reached 3.53% for a fusion of four detection models, resulting in an absolute fusion mAP of 83.65%. Moreover, the hyperparameters of our ensemble model may be adjusted to set an appropriate tradeoff between precision and recall to fit different recall and precision application requirements.

Original languageEnglish
Title of host publicationPattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Number of pages13
ISBN (Print)9783030414030
Publication statusPublished - 2020
Event5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand
Duration: 26 Nov 201929 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12046 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th Asian Conference on Pattern Recognition, ACPR 2019
Country/TerritoryNew Zealand


  • Deep learning
  • Ensemble learning
  • Fusion
  • Pedestrian detection


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