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Robust Pedestrian Detection: Faster Deployments with Fusion of Models

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

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Abstract

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.

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Acknowledgments

This work was funded by the Science and Technology Development Fund of Macau SAR (File no. 138/2016/A3).

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Authors and Affiliations

  1. School of Applied Sciences, Macao Polytechnic Institute, Macao S.A.R., China

    Chan Tong Lam, Jose Gaspar, Wei Ke, Xu Yang & Sio Kei Im

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  1. Chan Tong Lam
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  2. Jose Gaspar
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  3. Wei Ke
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  4. Xu Yang
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Correspondence to Chan Tong Lam .

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Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Lam, C.T., Gaspar, J., Ke, W., Yang, X., Im, S.K. (2020). Robust Pedestrian Detection: Faster Deployments with Fusion of Models. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-41404-7_11

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