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Pedestrian Detection Using Pixel Difference Matrix Projection
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DOI：10.1109/TITS.2019.2910093.png)
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Abstract
En 中文
Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance when a small-medium scale of training data with low-resolution images is available. To address this issue, a difference matrix projection (DMP) is developed to compute aggregated multi-oriented pixel differences using global matrix operations. Both the pixel differences and aggregation are computed using global matrix projection to avoid the laborious iterative operations. We tested our method on the INRIA, Daimler Chrysler classification (Daimler-CB), NICTA, and Caltech Pedestrian datasets. The experiments on these benchmark data sets show encouraging results in terms of detection performance, particularly for image datasets with low-resolution pedestrians.
Keywords:
Feature extraction
Detectors
Histograms
Shape
Intelligent transportation systems
Video surveillance
Task analysis
Pedestrian detection
pixel difference features
feature projection
HOG
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