WAR Activities Detection
YOLOv3-based object detection and behavior classification system for video analysis with 91% improvement.
Overview
An applied computer vision pipeline combining YOLOv3 object detection with temporal behavior classification for analyzing activities in war-zone video footage. The system processes video frames through a multi-stage pipeline: object detection, tracking, and behavior classification using deep learning models.
Achieved a 91% performance improvement over baseline methods. The project involved extensive dataset curation, model training/validation, and GPU-accelerated inference optimization.
Key Features
Technical Highlights
91% performance improvement over baseline detection methods
Published research contributing to academic knowledge base
Developed custom dataset curation and annotation workflow