Feature Extraction using Downsampling for Person Re-identification with Low-Resolution Images


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[Abstract]
We investigate whether a downsampling process of high-resolution pedestrian images can improve person re-identification accuracy. Generally, deep-learning and machine-learning techniques are used to extract features that are unaffected by image resolution. However, it requires a large number of pairs of high- and low-resolution images acquired from the same person. Here, we consider a situation in which these resolution pairs cannot be collected. We extract features from low-resolution pedestrian images using only a simple downsampling process that requires no training resolution pairs. We collected image resolution datasets by changing the focal length of the camera lens and the distance from the person to the camera. We confirmed that the person re-identification accuracy of the downsampling process was superior to that of the upsampling. We also confirmed that the low-frequency components corresponding to the output of the downsampling process contain many discriminative features.
[Publications]



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