Detecting Human Activities for Surveillance Purpose using Multi Scale Feature Maps

Today Object Detection and classifying objects inside a single frame that contains many objects is a time- consuming task. The accuracy rate has grown dramatically as a result of the deep learning technique. Despite new flight control laws, Unmanned Aerial Vehicles (UAVs) continue to grow in popularity for civilian and military uses, as well as personal use. This growing interest has accelerated the development of effective collision avoidance technologies. Such technologies are crucial for UAV operation, particularly in congested skies. Due to the cost and weight constraints of UAV payloads, camera-based solutions have become the de facto standard for collision avoidance navigation systems. This requires multi target detection techniques from video that can be effectively run on board.A drone is a quad copter with on board sensors. This drone can be controlled using wi-fi and laptop, usingPython and a Python library drone kit. This paper discusses a way for tracking a specific object known as object detection tracking methods, which may be used to track any arbitrary object chosen by the user, the camera of drone is used to take video frames along with all the sensor’s information such as ultrasonic sensors, GPS etc.A training model will identify an object first and determines the direction at which the drone should fly so that it keeps following person.