A Deep Learning-Enhanced Multi-Modal Sensing Platform for Robust Human Object Detection and Tracking in Challenging Environments

Abstract

In modern security situations, tracking multiple human objects in real-time within challenging urban environments is a critical capability for enhancing situational awareness, minimizing response time, and increasing overall operational effectiveness. Tracking multiple entities enables informed decision-making, risk mitigation, and the safeguarding of civil-military operations to ensure safety and mission success. This paper presents a multi-modal electro-optical/infrared (EO/IR) and radio frequency (RF) fused sensing (MEIRFS) platform for real-time human object detection, recognition, classification, and tracking in challenging environments. By utilizing different sensors in a complementary manner, the robustness of the sensing system is enhanced, enabling reliable detection and recognition results across various situations. Specifically designed radar tags and thermal tags can be used to discriminate between friendly and non-friendly objects. The system incorporates deep learning-based image fusion and human object recognition and tracking (HORT) algorithms to ensure accurate situation assessment. After integrating into an all-terrain robot, multiple ground tests were conducted to verify the consistency of the HORT in various environments. The MEIRFS sensor system has been designed to meet the Size, Weight, Power, and Cost (SWaP-C) requirements for installation on autonomous ground and aerial vehicles.

Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

“…Additionally, TIR is sensitive to temperature changes, making it useful for detecting thermal variations in objects. In fact, studies have shown that infrared thermal cameras are better at identifying car and bicycle objects compared to visual light cameras, particularly in nighttime operations [2]. This highlights the importance of using infrared thermal data for object detection, as it provides superior performance compared to visual light cameras in identifying objects at night.…”