Intelligent video surveillance beyond robust background modeling
作者：
Highlights：
• Extremely complex environments: sudden illumination changes are tackled.
• Evaluation metrics for perimeter protection solutions are analyzed.
• Constraints to classify false positives are learnt from example; no hand-crafted rules.
• Global features are extracted to make machines learn complex scenes.
• Experiments to verify our proposal have been conducted.
摘要
•Extremely complex environments: sudden illumination changes are tackled.•Evaluation metrics for perimeter protection solutions are analyzed.•Constraints to classify false positives are learnt from example; no hand-crafted rules.•Global features are extracted to make machines learn complex scenes.•Experiments to verify our proposal have been conducted.
论文关键词：Video surveillance,Video,Intrusion detection,Global features,Machine learning,Event,Recognition
论文评审过程：Received 10 April 2017, Revised 10 August 2017, Accepted 31 August 2017, Available online 1 September 2017, Version of Record 6 September 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.08.052
