Detection-Aware Trajectory Generation for a Drone Cinematographer

TL;DR

This paper presents a novel trajectory generation method for a drone cinematographer that optimizes for target detectability, enabling better object detection and tracking during dynamic chasing scenarios.

Contribution

It introduces a detectability-aware trajectory planning approach that actively guides the drone to improve target visibility and detection performance.

Findings

Enhanced object detection accuracy with the proposed trajectory.

Real-time trajectory updates respond to target motion.

Efficient path computation using DAG and quadratic programming.

Abstract

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.