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  • ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing

This content will become publicly available on June 16, 2026

Title: ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing
Effective training and debriefing are critical in high-stakes, mission-critical environments such as firefighting, where precision and error minimization are paramount. The traditional post-training analysis relies on the manual review of 2D video, a process that is time-consuming and lacks comprehensive situational awareness. To address these limitations, we introduce ACT360, a novel system that leverages 360-degree video and machine learning for automated action detection and efficient debriefing. ACT360 incorporates 360YOWO, a customized You Only Watch Once (YOWO) model enhanced with a spatial attention mechanism and equirectangular-aware convolution (EAC) to handle the unique distortions of panoramic video data. To enable deployment in resource-constrained environments, we apply quantization and model pruning, reducing the model size by 74% while maintaining robust accuracy (mAP drop of only 1.5 %, from 0.865 to 0.850) and improving inference speed. We validate our approach on a new, publicly available dataset of 55 labeled 360-degree videos covering seven key firefighting actions, recorded across various real-world practice sessions and environmental conditions. Furthermore, we integrate the pipeline with 360AIE (Action Insight Explorer), a web-based interface that provides automatic action detection, retrieval, and textual summarization of key events using large language models (LLMs), significantly improving post-incident analysis efficiency. ACT360 serves as a generalized framework for mission-critical debriefing, incorporating techniques such as EAC, spatial attention, summarization, and model optimization. These innovations apply to any training environment requiring lightweight action detection and structured nost-exercise analysis.  more » « less
Award ID(s):
2140645 2106592 1900875
PAR ID:
10635552
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3315-8646-1
Page Range / eLocation ID:
34 to 41
Subject(s) / Keyword(s):
Action Detection Distortion Optimal Model Attention Mechanism Model Size Spatial Attention Manual Review Training Environment Spatial Attention Mechanism 2D Video 360-degree Video Latitude Emergency Response Convolutional Layers Nighttime Exercise Training Object Detection Convolution Operation Action Recognition 2D Model Relevant Regions Inference Time Mission-critical Applications Video Understanding Google Cloud Platform Temporal Model Manual Selection Accuracy Of Feature Extraction
Format(s):
Medium: X
Location:
Cork, Ireland
Sponsoring Org:
National Science Foundation
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