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Small-Scale and Occluded Pedestrian Detection Using Multi Mapping Feature Extraction Function and Modified Soft-NMS.

Addis Abebe Assefa1, Wenhong Tian1, Kingsley Nketia Acheampong1

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China.

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Summary
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This study enhances pedestrian detection for autonomous driving by improving feature extraction and non-maximum suppression. The new method effectively identifies small and occluded pedestrians, crucial for intelligent transportation systems.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Pedestrian detection is critical for autonomous driving and intelligent transportation systems to prevent accidents.
  • Detecting small-scale and occluded pedestrians remains a significant challenge due to poor utilization of low-level features and limitations in standard detection algorithms.
  • Existing methods struggle with issues like stochastic weight initialization and greedy non-maximum suppression, leading to high miss rates.

Purpose of the Study:

  • To develop an improved pedestrian detection method capable of accurately identifying small and occluded pedestrians.
  • To address the challenges of ineffective feature utilization and the limitations of greedy non-maximum suppression in current systems.
  • To enhance the robustness and accuracy of pedestrian detection in complex scenarios.

Main Methods:

  • Proposed a multifocus feature extractor by fusing Gaussian and Xavier mapping feature maps to enlarge the effective receptive field.
  • Implemented focused attention feature selection on higher-layer feature maps of the Single Shot Detector (SSD) region proposal module, integrating them with low-layer features to preserve detail.
  • Introduced a decaying non-maximum suppression function that considers score and Intersection over Union (IOU) to mitigate high miss rates.

Main Results:

  • The proposed method demonstrated significant improvements in detecting small and occluded pedestrians on the Caltech pedestrian dataset.
  • Experimental results validated the effectiveness of the multifocus feature extractor and the decaying non-maximum suppression function.
  • The attention-based feature fusion successfully tackled the vanishing of feature details caused by convolutional and pooling operations.

Conclusions:

  • The developed approach effectively enhances pedestrian detection, particularly for challenging cases involving small and occluded individuals.
  • The novel feature extraction and suppression techniques offer a promising solution for improving the safety and reliability of autonomous driving systems.
  • This research contributes to advancing intelligent transportation systems by providing a more accurate and robust pedestrian detection framework.