计算机科学
人工智能
事件(粒子物理)
计算机视觉
目标检测
卷积神经网络
模式识别(心理学)
异常检测
特征(语言学)
深度学习
作者
Roberto Leyva,Victor Sanchez,Chang-Tsun Li
标识
DOI:10.1109/iwbf.2017.7935096
摘要
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.
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