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.

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

“…Two-point differencing initialization has been applied to target tracking by a drone [27][28][29]. The initial state and covariance of target t are obtained, respectively, aŝ…”

“…Finally, all tracks are tested for validity in terms of the length of the track life. The length of the track life is defined as the number of frames in between including the last frame updated by the measurement and the initial frame [26,27].…”

“…The interacting multiple model (IMM) estimator using multiple Kalman filters has been developed [25] and successfully applied to track multiple high maneuvering targets [26]. Recently, multiple moving vehicles were successfully tracked by a flying multirotor with a visual camera [27]. Data association, which assigns measurements to tracks, is also an important task for tracking multiple targets in a cluttered environment.…”

“…Data association, which assigns measurements to tracks, is also an important task for tracking multiple targets in a cluttered environment. The nearest neighbor (NN) measurement-track association is the most effective in computing and has been successfully applied to precision target tracking [27][28][29].…”

“…The minimum target speed was set to eliminate false tracks caused by stationary objects in heavy false alarm environments. Finally, a validity test is performed to check the continuity of the track [27]. Figure 1 shows a block diagram of detecting and tracking people with thermal video captured by a multirotor.…”