Fire SM: new dataset for anomaly detection of fire in video surveillance

Abstract

Tiny datasets of restricted range operations, as well as flawed assessment criteria, are currently stifling progress in video anomaly detection science. This paper aims at assisting the progress of this research topic, incorporating a wide and diverse new dataset known as Fire SM. Further, additional information can be derived by a precise estimation in early fire detection using an indicator, Average Precision. In addition to the proposed dataset, the investigations under anomaly situations have been supported by results. In this paper different anomaly detection methods that offer efficient way to detect Fire incidences have been compared with two existing popular techniques. The findings were analysed using Average Precision (AP) as a performance measure. It indicates about 78 % accuracy on the proposed dataset, compared to 71 % and 61 % on Foggia dataset, for InceptionNet and FireNet algorithm, respectively. The proposed dataset can be useful in a variety of cases. Findings show that the crucial advantage is its diversity.