# Moving Vehicle Tracking with a Moving Drone Based on Track Association
###### Abstract  
The drone has played an important role in security and surveillance. However, due to the limited computing power and energy resources, more efficient systems are required for surveillance tasks. In this paper, we address detection and tracking of moving vehicles with a small drone. A moving object detection scheme has been developed based on frame registration and subtraction followed by morphological filtering and false alarm removing. The center position of the detected object area is the input to the tracking target as a measurement. The Kalman filter estimates the position and velocity of the target based on the measurement nearest to the state prediction. We propose a new data association scheme for multiple measurements on a single target. This track association method consists of the hypothesis testing between two tracks and track fusion through track selection and termination. We reduce redundant tracks on the same target and maintain the track with the least estimation error. In the experiment, drones flying at an altitude of 150 m captured two videos in an urban environment. There are a total of 9 and 23 moving vehicles in each video; the detection rates are 92% and 89%, respectively. The number of valid tracks is significantly reduced from 13 to 10 and 56 to 26 in the first and the second video, respectively. In the first video, the average position RMSE of two merged tracks are improved by 83.6% when only the fused states are considered. In the second video, the average position and velocity RMSE are 1.21 m and 1.97 m/s, showing the robustness of the proposed system. d 2021 29 April 20  
The deep learning-based object detector and tracker require heavy computation with massive training data [11; 12], thus real-time processing can often be an issue to solve. The other is based on conventional image processing techniques. In [13], the moving objects are detected by background subtraction and the continuously adaptive mean-shift tracking and the optical flow tracking were applied to the video sequences acquired by the UAV. The particle filtering combined with the mean-shift method was developed to track a small and fast-moving object in [14]. A correlation-based tracker was proposed to track ground objects at low altitudes [15]. These methods also need to transmit high resolution video streams to the ground or impose high computing on the drone. Indeed, the visual trackers rarely evaluate the kinematic state of the target, such as position and velocity. The initial location, target size, and number of targets are often assumed to be known to the visual tracker. The tracking of high-resolution objects has been studied in [16; 17], but they are not suitable for wide range surveillance. The accurate position of the target can be obtained with non-imaging sensing data such as radar signals [18]. In [19], 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 [20]. However, in those methods, the drone or vehicles should be equipped with high-cost sensors that add more payload to the drone. Multiple targets are tracked by continuously estimating their kinematic state such as position, velocity, and acceleration. Highly maneuvering, closely located targets, heavy clutters, and low detection probability are challenges in multiple target tracking [21]. The Kalman filter is known to be optimal under independent Gaussian noise assumption in estimating the target state [22]. The interacting multiple model (IMM) estimator was developed [23] and successfully applied to handle multiple targets in high maneuvering [24]. Data association, which assigns measurements to tracks, is also an important task for tracking multiple targets in a clutter environment. The nearest neighbor (NN) measurement-track association is the most effective in computing and has been successfully applied to precision target tracking based on multiple frames [25; 26; 27].