Automatic Event Detection for Signal-based Surveillance
This is an incremental review addressing the problem of automating surveillance for public safety, but it does not present new methods or results.
The paper provides an overview of automatic event detection techniques for signal-based surveillance systems like CCTV, highlighting that despite research popularity, real-world deployment remains challenging due to technical and experimental factors.
Signal-based Surveillance systems such as Closed Circuits Televisions (CCTV) have been widely installed in public places. Those systems are normally used to find the events with security interest, and play a significant role in public safety. Though such systems are still heavily reliant on human labour to monitor the captured information, there have been a number of automatic techniques proposed to analysing the data. This article provides an overview of automatic surveillance event detection techniques . Despite it's popularity in research, it is still too challenging a problem to be realised in a real world deployment. The challenges come from not only the detection techniques such as signal processing and machine learning, but also the experimental design with factors such as data collection, evaluation protocols, and ground-truth annotation. Finally, this article propose that multi-disciplinary research is the path towards a solution to this problem.