Low-Complexity and Reliable Moving Objects Detection and Tracking for Aerial Video Surveillance with Small UAVS

Moving objects detection and tracking is the first and enabling step for many high-level UAV surveillance tasks, including cooperative UAV path planning, navigation control, and automated information analysis. In this work, we develop a low-complexity and reliable moving object detection algorithm by exploring the ideas of uncertainty analysis and spatiotemporal activity clustering. More specifically, we will develop a fast and efficient algorithm to estimate the global vehicle-camera motion. Image regions (blocks) with local motion will be detected using statistical hypothesis testing. Using spatiotemporal clustering, we will group these moving blocks into moving objects with physical meanings, such as moving vehicles or persons. Our extensive experimental results will demonstrate the efficiency of the proposed algorithm.