Global MotionAware Robust Visual Object Tracking for Electro Optical Targeting Systems Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electrooptical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pantilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.

Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely controlling a drone while filming a moving target usually requires multiple expert human operators; hence the need for an autonomous cinematographer. Current approaches have severe reallife limitations such as requiring fully scripted scenes, highprecision motioncapture systems or GPS tags to localize targets, and prior maps of the environment to avoid obstacles and plan for occlusion.In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a visionbased algorithm for target localization; (2) a realtime incremental 3D signeddistance map algorithm for occlusion and safety computation; and (3) a realtime camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines. We evaluate robustness and realtime performance in series of field experiments and simulations by tracking dynamic targets moving through unknown, unstructured environments. Finally, we verify that despite removing previous limitations, our system achieves stateoftheart performance.