Aerial surveillance of public areas with autonomous track and follow using image processing

The recent terror incidents make us question if the existing surveillance systems are still the best solution. CCTVs have proven to be a solution for large scale surveillance but when it comes to solving crimes, CCTVs have played a very minimal role. The concept that is proposed in this paper is an idea that is set to overcome these shortcomings and revolutionize the surveillance systems. Based on the framework of a quadcopter with autonomous flight capabilities and auto-tracking feature, the drone uses image processing algorithm of Probability Hypothesis Density (PHD) filtering using a Markov Chain Monte Carlo (MCMC) implementation. To efficiently control the swarm of quadcopters we use an Energy Efficient Coverage Path Planning (EECPP) problem. The concept explained in this paper integrates a swarm of drones which can act autonomously with Image processing and can be the key for the future of public monitoring and security when made into a full scale device, saving precious lives at times.