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Detection and Confirmation of Multiple Human Targets Using Pixel-Wise Code Aperture Measurements.

Chiman Kwan¹, David Gribben¹, Akshay Rangamani²

¹Applied Research LLC, Rockville, MD 20850, USA.

Journal of Imaging

|August 30, 2021

Summary

This study introduces a novel method for detecting human targets directly from compressive video measurements, reducing processing time and data loss. The approach shows promise for long-range surveillance but requires further development for reliable target confirmation.

Keywords:

ResNet YOLO classification compressive measurement detection pixel-wise code aperture

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Area of Science:

Computer Vision
Signal Processing
Machine Learning

Background:

Conventional target detection requires full video reconstruction, which is time-consuming and can lead to information loss.
Compressive sensing offers potential for efficient video data handling by reducing bandwidth and storage requirements.

Purpose of the Study:

To apply a novel compressive sensing approach for direct human target detection and classification within the measurement domain.
To evaluate the feasibility of detecting human targets using raw videos from a pixel-wise code exposure (PCE) camera.

Main Methods:

Utilized a pixel-wise code exposure (PCE) camera to capture condensed video frames.
Employed a combination of You Only Look Once (YOLO) and Residual Network (ResNet) deep learning algorithms for detection and confirmation.
Tested the framework on optical and mid-wave infrared (MWIR) videos from the SENSIAC database.

Main Results:

The proposed framework successfully demonstrated feasible human target detection up to 1500 meters.
The method operates directly on compressive measurements, bypassing the need for full reconstruction.

Conclusions:

Direct detection in the compressive domain is a viable strategy for efficient target identification.
Further research is needed to enhance the target confirmation capabilities of the proposed framework for practical applications.