Abstract
A small drone is capable of capturing distant objects at a low cost. In this paper, long distance (up to 1 km) ground target tracking with a small drone is addressed for oblique aerial images, and two novel approaches are developed. First, the coordinates of the image are converted to real-world based on the angular field of view, tilt angle, and altitude of the camera. Through the image-to-position conversion, the threshold of the actual object size and the center position of the detected object in real-world coordinates are obtained. Second, the track-to-track association is improved by adopting the nearest neighbor association rule to select the fittest track among multiple tracks in a dense track environment. Moving object detection consists of frame-to-frame subtraction and thresholding, morphological operation, and false alarm removal based on object size and shape properties. Tracks are initialized by differencing between the two nearest points in consecutive frames. The measurement statistically nearest to the state prediction updates the target’s state. With the improved track-to-track association, the fittest track is selected in the track validation region, and the direction of the displacement vector and velocity vectors of the two tracks are tested with an angular threshold. In the experiment, a drone hovered at an altitude of 400 m capturing video for about 10 s. The camera was tilted 30° downward from the horizontal. Total track life (TTL) and mean track life (MTL) were obtained for 86 targets within approximately 1 km of the drone. The interacting multiple mode (IMM)-CV and IMM-CA schemes were adopted with varying angular thresholds. The average TTL and MTL were obtained as 84.9–91.0% and 65.6–78.2%, respectively. The number of missing targets was 3–5; the average TTL and MTL were 89.2–94.3% and 69.7–81.0% excluding the missing targets.
Highlights
Small unmanned aerial vehicles (UAVs), or drones, are useful for security and surveillance [1,2]
A total of 86 targets within approximately 1 km of the drone were investigated with total track life (TTL) and mean track life (MTL)
The interacting multiple mode (IMM)-CV and IMM-CA are adopted with the image-to-position conversion and the proposed directional track association procedure
Summary
Small unmanned aerial vehicles (UAVs), or drones, are useful for security and surveillance [1,2]. A small drone captures video from a distance at a low cost [3]. Ground targets can be tracked with a small drone with visual, nonvisual, or combined methods. Tracking performance can be degraded by small objects, large numbers of targets, and camera motion [5]. The background subtraction and the adaptive mean-shift and optical flow tracking were developed for the video sequences captured by a drone in [10]. The mean-shift tracker based on particle filtering was utilized to track a small and fast-moving object in [11]. The kernelized correlation filter-based target tracking was studied in [13]. A total of 86 targets within approximately 1 km of the drone were investigated with total track life (TTL) and mean track life (MTL). More detailed processes of target tracking are described in [22,23,24]
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