Journal
PATTERN RECOGNITION
Volume 64, Issue -, Pages 187-201Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2016.09.016
Keywords
Anomaly detection; Video surveillance; Unsupervised learning
Funding
- National Natural Science Foundation of China (NSFC) [61673030, 61340046, 60875050, 60675025]
- National High Technology Research and Development Program of China (863 Program) [2006AA04Z247]
- Scientific Research Project of Guangdong Province [2015B010919004]
- National high level talent special support program
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Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the behavior patterns far away from our model are labeled as anomalous, for which far away is measured by a time varying threshold. Experiments are implemented on three surveillance datasets, namely UMN, UCSD Ped1/Ped2 and Avenue dataset. All datasets have changing scenes due to mutable crowd density and behavior types. Anomaly detection results show that our model can adapt to the current scene rapidly and reduce false alarms while still detecting most anomalies. Quantitative comparisons with 12 recent approaches further confirm our superiority.
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