Night-Time Fire Detection in Grayscale Surveillance Videos Based on Flame Patterns and Brightness Gradients

Fire detection in low-resolution surveillance videos at night is challenging under poor illumination. Conventional methods rely on colour information or computationally intensive deep-learning models, potentially limiting their suitability for grayscale video and resource-constrained environments. This study presents a night-time fire detection method that operates on grayscale video frames and jointly models temporal brightness fluctuations and spatial motion cues using a motion-based temporal representation and a brightness-gradient support vector machine (SVM) classifier. A sliding temporal window aggregates frame-level evidence, thereby reducing isolated false detections and providing more stable fire-event decisions over time. Experiments on the UCF-Crime arson and normal subsets (30 FPS) demonstrate promising video-level fire detection, with all evaluated fire-containing videos correctly identified. The method also shows promising classification performance on normal videos. At the frame level, the mean per-video false alarm ratio and miss rate across fire-containing videos are 4.37 ± 13.69% and 14.00 ± 11.24%, respectively. Ablation results for fire-containing videos show that the full system achieves a pooled frame-level miss rate of 9.51% and an FP-to-ground-truth fire-frame ratio of 0.76%. The CPU-only system processes a single video stream at 32–34 FPS while updating all 12 visualisation panels, demonstrating its potential for real-time surveillance.