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Scale-Sensitive Feature Reassembly Network for Pedestrian Detection.

Xiaoting Yang1, Qiong Liu1

  • 1School of Software Engineering, South China University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
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This study introduces a novel Scale-Sensitive Feature Reassembly Network (SSNet) to improve pedestrian detection by addressing scale variation challenges. SSNet enhances feature representation and context fusion for more accurate detection of pedestrians in road scenes.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Pedestrian detection faces significant challenges due to scale variation.
  • Existing feature pyramid networks suffer from information loss and inadequate backbone sampling.
  • Arbitrary region of interest (RoI) allocation leads to coarse RoI representation, especially for small pedestrians.

Purpose of the Study:

  • To propose a novel Scale-Sensitive Feature Reassembly Network (SSNet) for improved pedestrian detection in road scenes.
  • To enhance the sensitivity to pedestrians across multiple scales and mitigate information loss.
  • To achieve more accurate pedestrian detection by refining RoI representation.

Main Methods:

  • A multi-parallel branch sampling module with flexible receptive fields and adjustable anchor stride was developed.
Keywords:
RoI featurefeature fusionpedestrian detectionroad scenescale variation

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  • A context enhancement fusion module was introduced to inject spatial context information and reduce feature loss.
  • An adaptive reassembly strategy was designed for recognizable RoI features in the proposal refinement stage.

    • Main Results:

    • The proposed SSNet significantly surpasses the baseline method in pedestrian detection.
    • The network demonstrates competitive performance compared to existing methods.
    • Experiments on CityPersons and Caltech datasets validate the effectiveness of SSNet.

    Conclusions:

    • SSNet effectively addresses scale variation in pedestrian detection through its novel modules.
    • The integration of lightweight modules enhances detection accuracy and efficiency.
    • The proposed method offers a robust solution for real-world pedestrian detection applications.