A face mask detection system: An approach to fight with COVID-19 scenario

CoSumNet: A video summarization-based framework for COVID-19 monitoring in crowded scenes

The outbreak of COVID-19 pandemic poses new challenges to research community to investigate novel mechanisms for monitoring as well as controlling its further spread via crowded scenes. Moreover, the contemporary methods of COVID-19 preventions are enforcing strict protocols in the public places. The emergence of robust computer vision-enabled applications leverages intelligent frameworks for monitoring of the pandemic deterrence in public places. The employment of COVID-19 protocols via wearing face masks by human is an effective procedure that is implemented in several countries across the world. It is a challenging task for authorities to manually monitor these protocols particularly in densely crowded public gatherings such as, shopping malls, railway stations, airports, religious places etc. Thus, to overcome these issues, the proposed research aims to design an operative method that automatically detects the violation of face mask regulation for COVID-19 pandemic. In this research work, we expound a novel technique for COVID-19 protocol desecration via video summarization in the crowded scenes (CoSumNet). Our approach automatically yields short summaries from crowded video scenes (i.e., with and without mask human). Besides, the CoSumNet can be deployed in crowded places that may assist the controlling agencies to take appropriate actions to enforce the penalty to the protocol violators. To evaluate the efficacy of the approach, the CoSumNet is trained on a benchmark "Face Mask Detection ∼12K Images Dataset" and validated through various real-time CCTV videos. The CoSumNet demonstrates superior performance of 99.98 % and 99.92 % detection accuracy in the seen and unseen scenarios respectively. Our method offers promising performance in cross-datasets environments as well as on a variety of face masks. Furthermore, the model can convert the longer videos to short summaries in nearly 5-20 s approximately.