HitoMi-Cam: A Shape-Agnostic Person Detection Method Using the Spectral Characteristics of Clothing

TL;DR

HitoMi-Cam is a real-time, shape-agnostic person detection method leveraging spectral clothing properties, demonstrating high accuracy and efficiency on resource-limited devices, especially useful in unpredictable environments like disaster rescue.

Contribution

This paper introduces HitoMi-Cam, a novel spectral-based person detection system that operates efficiently on edge devices and performs well in shape-agnostic scenarios, complementing CNN methods.

Findings

Achieves 23.2 fps on resource-constrained hardware.

Surpasses CNNs with 93.5% average precision in rescue scenarios.

Maintains minimal false positives across evaluations.

Abstract

While convolutional neural network (CNN)-based object detection is widely used, it exhibits a shape dependency that degrades performance for postures not included in the training data. Building upon our previous simulation study published in this journal, this study implements and evaluates the spectral-based approach on physical hardware to address this limitation. Specifically, this paper introduces HitoMi-Cam, a lightweight and shape-agnostic person detection method that uses the spectral reflectance properties of clothing. The author implemented the system on a resource-constrained edge device without a GPU to assess its practical viability. The results indicate that a processing speed of 23.2 frames per second (fps) (253x190 pixels) is achievable, suggesting that the method can be used for real-time applications. In a simulated search and rescue scenario where the performance of CNNs declines, HitoMi-Cam achieved an average precision (AP) of 93.5%, surpassing that of the compared CNN models (best AP of 53.8%). Throughout all evaluation scenarios, the occurrence of false positives remained minimal. This study positions the HitoMi-Cam method not as a replacement for CNN-based detectors but as a complementary tool under specific conditions. The results indicate that spectral-based person detection can be a viable option for real-time operation on edge devices in real-world environments where shapes are unpredictable, such as disaster rescue.