# Moving Vehicle Tracking with a Moving Drone Based on Track Association
###### Abstract
In the paper, we propose a new association scheme, track-track association, in order to maintain a single track for each target. In the proposed method, first, it is an association test of two tracks that performs hypothesis testing using chi-square statistics, which is the statistical distance between estimates of the current state of the two tracks. Second, the track with the smallest determinant of the covariance matrix of the state is maintained and the other is terminated. It is noted that covariance measures the expected value of the squared error between the true state and the unbiased estimate. Finally, the last update of the selected track is replaced by the fused estimate. We also set the criteria for a valid track and the termination of the track. A track is confirmed as a valid track if it continues longer than the minimum track life, and a track is terminated after searching for the measurements for a certain number of allowed frames. In the experiments, the drones captured two videos while flying at a height of 150 m in an urban environment. The drone camera pointed directly downwards. In each video, there are a total of 9 or 23 moving vehicles (cars, buss, motorcycles, and bicycles), respectively. With the proposed association, the number of valid tracks was reduced from 13 to 10 in the first video and 56 to 26 in the second video. In the first video (Video 1), considering only the fusion state of the two targets, the sum of the position RMSE decreased from 5.2 m to  
Figure 1: Illustration of the surveillance system by small drones. 0.85 m, showing a reduction rate of about 83.6%. In the second video (Video 2), the average position and velocity RMSEs are 1.21 m 1.97 and m/s, respectively, showing the accuracy of the proposed system. Figure 2 shows a block diagram of the moving vehicle detection and multiple target tracking. The contributions of this paper are listed as follows: (1) we propose a new data association scheme. The proposed track-track association is utilized with the NN measurement-track association. The track association is based on the least covariance matrix while the NN association is based on the least statistical distance. Therefore, the proposed data association is easy to implement and provides fast computing. The experimental results show that the proposed method is very effective when multiple measurements are detected on a single target in consecutive frames. (2) The moving object detection-multiple target tracking scheme is studied with a moving drone. In the previous studies, the video is captured with a drone hovering at a fixed position. However, as the drone flies, the coverage of surveillance expands by utilizing the drone as a dynamic sensor. A moving drone has a wider range of surveillance at higher altitudes and at higher speeds, which degrades image resolution and quality. Through the experiments, the proposed scheme was applied to small size objects at slow-rate frame videos (10 fps). (3) We propose a new configuration of drone surveillance as shown in Figure 1. Small drones have limited computational resources in CPU, memory, battery, and bandwidth. We can build a more efficient surveillance system where storage or transmission of high-resolution video streams is not essential. The remainder of the paper is organized as follows: object detection is discussed in Section 2. Section 3 demonstrates multiple target tracking. Section 4 presents experimental results, and the conclusion follows in Section 5.