Search

Today, in the technological era of the 21st century, CCTV cameras have been proven to be very fruitful in our daily lives. From monitoring the baby in the bassinet to prevent some crimes, CCTV camera has become of vital importance. We as humans, always try to make things perfect around us. Using this article, we also have attempted to present our perspective to make these CCTV cameras more perfect. We have made an effort to enhance regular CCTV cameras using the vast field of deep learning and IoT. We have attempted to accomplish our goal by providing a protoStype for the smart surveillance system. We have tried to upgrade the regular CCTV cameras with some customized deep learning models developed by us. In this modified version, we have given the CCTV cameras the ability to detect fire and weapons. Also, we have tried to fulfil an ad-hoc requirement of Face Mask Detection considering the current situation of COVID19. For fulfilling our objective, we have provided an outline combining IoT (RaspberryPi) to deep learning using AWS EC2 Cloud Architecture. To make the surveillance system user-friendly, we have also taken account of the client-side interface. Considering all the above applications, we have successfully provided an archetype in this paper.

We present algorithms for solving multi-armed and linear-contextual bandit tasks in the face of adversarial corruptions in the arm responses. Traditional algorithms for solving these problems assume that nothing but mild, e.g., i.i.d. sub-Gaussian, noise disrupts an otherwise clean estimate of the utility of the arm. This assumption and the resulting approaches can fail catastrophically if there is an observant adversary that corrupts even a small fraction of the responses generated when arms are pulled. To rectify this, we propose algorithms that use recent advances in robust statistical estimation to perform arm selection in polynomial time. Our algorithms are easy to implement and vastly outperform several existing UCB and EXP-style algorithms for stochastic and adversarial multi-armed and linear-contextual bandit problems in wide variety of experimental settings. Our algorithms enjoy minimax-optimal regret bounds, as well as can tolerate an adversary that is allowed to corrupt upto a universally constant fraction of the arms pulled by the algorithm.