# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
###### Abstract  
The multirotor has the capability to capture distant objects. Because the computing resources of the multirotor are limited, efficiency is an important factor to consider. In this paper, multiple target tracking with a multirotor at a long distance (\(\sim\)400 m) is addressed; the interacting multiple model (IMM) estimator combined with the directional track-to-track association (abbreviated as track association) is proposed. The previous work of the Kalman estimator with the track association approach is extended to the IMM estimator with the directional track association. The IMM estimator can handle multiple targets with various maneuvers. The track association scheme is modified in consideration of the direction of the target movement. The overall system is composed of moving object detection for measurement generation and multiple target tracking for state estimation. The moving object detection consists of frame-to-frame subtraction of three-color layers and thresholding, morphological operation, and false alarm removing based on the object size and shape properties. The centroid of the detected object is input into the next tracking stage. The track is initialized using the difference between two nearest points measured in consecutive frames. The measurement nearest to the state prediction is used to update the state of the target for measurement-to-track association. The directional track association tests both the hypothesis and the maximum deviation between the displacement and directions of two tracks followed by track selection, fusion, and termination. In the experiment, a multirotor flying at an altitude of 400 m captured 55 moving vehicles around a highway interchange for about 20 s. The tracking performance is evaluated for the IMMs using constant velocity (CV) and constant acceleration (CA) motion models. The IMM-CA with the directional track association scheme outperforms other methods with an average total track life of 91.7% and an average mean track life of 84.2%. d rone surveillance; moving object detection; multiple target tracking; directional track association; state estimation +
Footnote †: journal: Medical Imaging  
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Footnote 1: email: {[[https://www.mdpi.com/journal/applsci/uplo/upl](https://www.mdpi.com/journal/applsci/uplo/upl)]([https://www.mdpi.com/journal/applsci/uplo/upl](https://www.mdpi.com/journal/applsci/uplo/upl))  
was developed to track a small and fast-moving object [10]. A fast target tracking method was developed based on the SIFT feature with multiple hypothesis tracking [11]. The visual trackers need to transmit high resolution video streams to the ground or impose high computing on the drone. The initial location, target size, and number of targets are often assumed to be known to the visual tracker. Indeed, the visual trackers rarely evaluate the kinematic state of the target. The kinematic state of the target can be obtained with non-imaging sensing data such as radar signals [12]. In [13], aerial video processing has been combined with the high-precision GPS data from vehicles. Bayesian fusion of vision and radio frequency sensors were studied for ground target tracking [14]. However, in the non-visual approach, the drone should be equipped with high-cost sensors that add more payload to the drone, or infrastructure is required on the ground or in the vehicle. Multiple targets are tracked by continuously estimating their kinematic state such as position, velocity, and acceleration. The Kalman filter is known to be optimal under independent Gaussian noise assumption in estimating the target state [15]. The interacting multiple model (IMM) estimator developed in [16] consists of the several mode-matched Kalman filters to handle different maneuvers of multiple targets. The track initialization corresponding to measurement-to-measurement association starts the track [17]. Measurement-to-track association combines measurements with a track to update the state of a target.