The LV dataset: A realistic surveillance video dataset for abnormal event detection

Abstract

In recent years, designing and testing video anomaly detection methods have focused on synthetic or unrealistic sequences. This has mainly four drawbacks: 1) events are controlled and predictable because they are usually performed by actors; 2) environmental conditions, e.g. camera motion and illumination, are usually ideal thus realistic conditions are not well reflected; 3) events are usually short and repetitive; and 4) the material is captured from scenarios that do not necessarily match the testing scenarios. This leads us to propose a new rich collection of realistic videos captured by surveillance cameras in challenging environmental conditions, the Live Videos (LV) dataset. We explore the performance of a number of state-of-the-art video anomaly detection methods on the LV dataset. Our results confirm the need to design methods that are capable of handling realistic videos captured by surveillance cameras with acceptable processing times. The proposed LV dataset, thus, will facilitate the design and testing of such new methods.