Detection-Aware Trajectory Generation for a Drone Cinematographer
This work addresses the challenge of improving object detection and tracking for drone cinematography, though it is incremental as it builds on existing methods with a specific focus on color-based detectability.
The paper tackles the problem of generating efficient drone trajectories for chasing dynamic targets by incorporating a color detectability objective to improve object detection and tracking performance, resulting in enhanced state-of-the-art algorithm performance.
This work investigates an efficient trajectory generation for chasing a dynamic target, which incorporates the detectability objective. The proposed method actively guides the motion of a cinematographer drone so that the color of a target is well-distinguished against the colors of the background in the view of the drone. For the objective, we define a measure of color detectability given a chasing path. After computing a discrete path optimized for the metric, we generate a dynamically feasible trajectory. The whole pipeline can be updated on-the-fly to respond to the motion of the target. For the efficient discrete path generation, we construct a directed acyclic graph (DAG) for which a topological sorting can be determined analytically without the depth-first search. The smooth path is obtained in quadratic programming (QP) framework. We validate the enhanced performance of state-of-the-art object detection and tracking algorithms when the camera drone executes the trajectory obtained from the proposed method.