Karunakar Thammadi

The Advanced Surveillance with Autonomous Drone Surveillance Analytics solution is a combination of advanced AI technologies, Computer Vision and Surveillance Analytics technologies that enhance security, monitoring and situational awareness. Traditional surveillance system has several drawbacks, including low coverage, low speed and high cost, especially when dealing with large-scale and complex monitoring scenarios. The goal of this project is to address these challenges by taking an AI-based autonomous surveillance analytics platform that can automatically detect and flag suspicious activities, intelligently. The main focus of the proposed system is on the integration of intelligent video surveillance (IVS) technology and object detection with the help of deep learning and computer vision techniques. Uploaded aerial surveillance videos are processed and analyzed on the platform through an intelligent application of machine learning algorithms, recognizing abnormal situations, entry without authorization, moving objects, vehicles and man. Real-time video analysis and object-recognition is possible with technologies like YOLO (You Only Look Once), OpenCV and Convolutional Neural Networks (CNNs).

Due to the environmental systems of various parts of the world, especially that of the Amazon rainforest, Africa, and Southeast Asia, illegal mining has been a key environmental problem in many areas. Illegal mining leads to massive destruction of the environment such as deforestation, river contamination, and wildlife habitat destruction. Late notice on illegal mining sites is part of the greatest problems that government and environmental agencies have to experience. When perilous activities may be noted, it is usually too late as serious harm to the environment may have been done. This project aims to create an artificial intelligence-based early warning system to detect destabilising illegal mining activities with the help of Sentinel-2 satellite images. It uses satellite data to detect environmental anomalies, vegetation changes and abnormal land-use patterns, related to illegal mining. Through the use of machine learning algorithms, anomalies in land development are detected and an early warning system is developed to assist environment managers to identify and monitor these areas.