# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
###### Abstract
Track-to-track association (abbreviated as track association) combines tracks to maintain a unique track for a target in a frame [18]. In the paper, an IMM-directional track association scheme is proposed for tracking multiple targets captured by a multirotor. The strategy developed in [19; 20; 21] extracts measurements during the object detection, and then establishes the tracks based on those measurements. This method does not directly use the intensity information for target tracking, thus there is no need to store or transmit high-resolution video streams. In [21], the track association based on the Kalam filter was developed; however, the IMM filter can efficiently handle the various maneuvers of a large number of targets. The track association only depends on the statistical distance of the two estimates of different tracks, but the spatial direction of the tracks can be considered to reduce false track association. In order to detect moving objects, a subtraction is performed between the current frame and the previous frame in all three-color layers, and thresholding follows to generate a binary image. Then, the morphological operation, closing (dilation and erosion) is applied to the binary image. The dilation operation connects the fragmented regions of one object while the erosion operation shrinks the dilated boundary. Finally, false detections are eliminated using the actual object size and two shape properties: squareness and rectangularity [22]. The centroid of the extracted area becomes the measured position for the next tracking stage. Tracking is performed by the IMM filter to estimate the kinematic states of the targets. Two discrete time kinematic models are used and compared. One is a (nearly) constant velocity (CV) motion model, and the other is a (nearly) constant acceleration (CA) motion model [23]. The CV model is equivalent with the white acceleration model since the accelerations are modeled as white noise. The Wiener-sequence acceleration model is adopted for the CA model assuming that the acceleration increment is a white noise process. The discretization of the continuous model is performed for the discrete model [24], but the Wiener sequence acceleration model is developed in discrete time directly [25]. With high quality measurements, the CA model can produce the accurate estimate of the acceleration [26]. The IMM with a combined CV and CA scheme was retrieved to track a single target [23]. The IMM-CV scheme was applied to track various maneuvering 120 aerial targets for the aerial early warning system [27]. A track is initialized by the two-point differencing initialization following the maximum speed gating. For the measurement-to-measurement and track association tasks, the gating process is first performed based on the chi-square hypothesis testing. Then, the measurement (or track) selection and update (fusion) are followed. The nearest neighbor (NN)measurement-to-track association is the most effective in computing and has been successfully applied to target tracking based on multiple frames captured by a drone [19; 20; 21; 22]. The track fusion method in multi-sensor environments was developed assuming a common process noise of the target [18]. In the previous work [21], a practical approach for the track association was proposed; after the hypothesis testing, only the track of the smallest determinant of the covariance matrix is maintained and the other is terminated. The current update of the selected track is replaced by the fusion estimate. In the paper, the directional track association is proposed to be equipped with the directional gating process. The directional gating tests the maximum deviation in the directions of the tracks and the direction of the displacement vector between the tracks. In other words, two tracks can be fused into one track only when the direction of the motion is close to the direction of the displacement. Several criteria exist for track termination.