Armed Activity Recognition using Pose-Based Features with Machine Learning and Deep Learning
Year: 2026
Type: article
Abstract: Automatic detection of "armed" activities commonly focuses on the detection of weapons, with the limitations of variety in shapes, types and colours of weapons. This study aims to perform AI-based identification of armed and unarmed activities using postural landmark features of the human body, without requiring weapon visibility. We conducted a comparative analysis on the postures by MediaPipe and YOLOv8-pose frameworks and their impact on the performance of conventional machine learning and deep learning classifiers. Posture landmarks were extracted separately using both frameworks on a total number of 3,866 images (1,934 armed and 1,932 unarmed activities). Conventional machine learning algorithms and Long Short-Term Memory (LSTM) algorithms were trained without data augmentation on GridSearchCV and the early stopping mechanism. The experimental results showed that the YOLOv8-pose-based posture feature landmarks provide higher armed pose activity classification performance than the MediaPipe-based landmarks. The optimized SVM trained on the YOLOv8-pose feature set achieved the highest test accuracy rate of 94.2%, whereas the most successful deep learning model, the YOLOv8-pose-based Bi-LSTM, reached an accuracy rate of 93.9%. Consequently, the findings demonstrate that feature set responsibility can outweigh model complexity of sequence learning-based algorithms in "armed pose" detection for even a lightweight SVM algorithm.
Authors: Gökhan Altan, Ramazan Daşcı
Institution: İskenderun Technical University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7142623878