Online Anomaly Detection over Live Social Video Streaming
Chengkun He Xiangmin Zhou Chen Wang Iqbal Gondal Jie Shao Xun Yi
Social video anomaly is an observation in video streams that does not conform
to a common pattern of dataset's behaviour. Social video anomaly detection
plays a critical role in applications from e-commerce to e-learning.
Traditionally, anomaly detection techniques are applied to find anomalies in
video broadcasting. However, they neglect the live social video streams which
contain interactive talk, speech, or lecture with audience. In this paper, we
propose a generic framework for effectively online detecting Anomalies Over
social Video LIve Streaming (AOVLIS). Specifically, we propose a novel deep
neural network model called Coupling Long Short-Term Memory (CLSTM) that
adaptively captures the history behaviours of the presenters and audience, and
their mutual interactions to predict their behaviour at next time point over
streams. Then we well integrate the CLSTM with a decoder layer, and propose a
new reconstruction error-based scoring function $RE_{IA}$ to calculate the
anomaly score of each video segment for anomaly detection. After that, we
propose a novel model update scheme that incrementally maintains CLSTM and
decoder. Moreover, we design a novel upper bound and ADaptive Optimisation
Strategy (ADOS) for improving the efficiency of our solution. Extensive
experiments are conducted to prove the superiority of AOVLIS.