# Moving Vehicle Tracking with a Moving Drone Based on Track Association
###### Abstract
The Bayesian data association approach, probabilistic data association (PDA) and joint PDA (JPDA), makes a soft decision on validated measurements using the association probability between the target and the measurement [21]. However, in the scheme, the tracks should be initialized in advance with prior knowledge of the number of targets. An efficient sub-event tracker based on the IMM-JPDA approach was proposed to reduce the computational load in [28]. Another Bayesian approach with hard decision, multiple hypotheses tracking (MHT) [29], requires hypothesis reduction due to exponentially increasing computational complexity. The probabilistic MHT (PMHT) was developed as soft association in [30]. In the scheme, the computational load increases linearly in the number of measurements and targets, but the number of targets should be known and fixed. The closed-form solution of the PMHT was proposed as the Gaussian Mixture PHD (GMPHD) in [31]. A non-Bayesian data association approach, \(N\)-dimension assignment, has been developed to make hard decision on the measurement [32]. However, the assignment technique assumes that, at most, one measurement is detected on a single target. When multiple sensors are available, the track-to-track fusion method has been developed, assuming multiple tracks arise from the common process noise of the target [21]. Various track-to-track fusion methods have been studied and verified in [33; 34]. In the paper, we address the detection and tracking of moving vehicles with a moving drone. Considering the limited computing power and communication resources of small drones, the drone surveillance system is configured as shown in Figure 1. In one case, a singe drone processes both moving object detection and multiple target tracking using an onboard computer. The kinematic states of the target or a simple alert signal is transmitted to the ground. In another case, moving object detection is performed on the drone instantly after video capture, and then the center positions of the extracted object areas are transmitted to the ground control station to perform multiple target tracking. In the latter, it is possible to associate the measurements of multiple drones with the proposedassociation method. Since the proposed scheme does not use intensity information with heavy computation, there is no need to store or transmit a high-resolution video stream. During moving object detection, frame registration is performed between two consecutive frames to compensate for the drone's movement [8]. The current frame compensated for and subtracted from the previous frame, and thresholding is performed to generate a binary image of the moving object. Two morphological filters (erosion and dilation) are applied sequentially to the binary image. The erosion filter removes very small clutters, and the dilation filter restores the boundaries of the object areas and connects fragmented areas of one object. Finally, target blobs smaller than the assumed target size are removed. The center location of the target area becomes the measurement of the next tracking stage. The tracking is performed by the Kalman filter to estimate the kinematic state of the target following the moving object detection. A nearly constant velocity (NCV) motion model is used as the discrete time kinematic model for the target. The NCV model has been successfully applied to the aerial early warning system to track 120 aerial targets with high maneuvers up to 6 g [24] and other applications [34; 35; 36]. The track is initialized by the two-point differential initialization following the maximum speed gating process. The initial state of the track can affect tracking performance severely. In the proposed scheme, no additional process for the initialization is required. For measurement of track association, a position gating process excludes measurements outside the validation region. Then, the NN association assigns valid measurements to tracks. However, multiple tracks can be generated if multiple measurements on a single target are detected [27].