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All Journal Journal of Internet and Software Engineering
Bowo Nugroho
Informatika, Institut Teknologi Kalimantan
Computer Science & IT

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Pose-Based Suspicious Activity Detection Using YOLOv12n and MediaPipe in Smart Surveillance Systems Bowo Nugroho; Bima Prihasto; Aninditya Anggari Nuryono; Ahmad Rusdianto Andarina Syakbani; Nawfal Alhadi; Rionando Soeksin Putra; Ilham Al Basith
Journal of Internet and Software Engineering Vol 7 No 1 (2026): Journal of Internet and Software Engineering
Publisher : Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jise.v7i1.31998

Abstract

Detecting suspicious activity in video surveillance is critical for public safety. Early identification of pre-incident behaviors, such as "looking around" nervously, can prevent crimes before they occur. However, traditional surveillance relies on continuous human monitoring, which is prone to error and resource-intensive. Existing automated methods struggle with real-time stability and produce flickering predictions. We present a two-stage pose-based detection system: YOLOv12n detects humans in each frame, and MediaPipe Holistic extracts pose features. A Support Vector Classifier (SVC) and Neural Network (NN) then classify 15-frame temporal segments as either "still" or "looking around." On our custom dataset, SVC achieves 97.35% accuracy while NN achieves 97.00%. Temporal smoothing eliminates prediction flickering, enabling stable real-time deployment. SVC provides better precision-recall balance, NN offers greater adaptability to complex pose variations.
Co-Authors Ahmad Rusdianto Andarina Syakbani Aninditya Anggari Nuryono Bima Prihasto Ilham Al Basith Nawfal Alhadi Rionando Soeksin Putra