- freely available
Remote Sens. 2019, 11(19), 2278; https://doi.org/10.3390/rs11192278
- First of all, we proposed a novel multiple-target-tracking algorithm which is based on dense-optical-flow-trajectory voting. The algorithm models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID. In the method, we first generated the optical-flow trajectories of the target and performed ID voting on the optical-flow trajectories of each target. Then, voting results were used to measure the similarity of objects in adjacent frames, and tracking results were obtained by data association. Since the optical-flow trajectory of the target could accurately reflect the position change of the target with time, regardless of the appearance, shape, and size of the target, the algorithm could enhance tracking performance in the aerial video, even at a low frame rate.
- Second, we built a training dataset and a test dataset for deep-learning vehicle-detection in aerial videos. The training dataset contained many self-captured aerial images, and we used the LabelImg tool to label vehicle targets in these aerial images. The test dataset was collected by our UAV system and included four kinds of surveillance scenarios and multiple video frame rates. The UAV system was composed of DJI-MATRICE 100 and a monocular point-gray camera. Due to the different UAV shooting angles and heights, vehicle sizes in the images are various, and the background is complex. Based on this, we could generate the network model of the deep-learning algorithm and obtain good vehicle-detection results, which is the basis of multiple-target tracking.
- Finally, we conducted a large number of experiments on the test dataset to prove the effectiveness and robustness of the algorithm. The experiment results on aerial video with various environments and different frame rates show that our algorithm could obtain effective and robust tracking results in various complex environments, and the tracking effect was still robust when there were problems such as target adhesion, low frame rate, and small target size. In addition, we carried out qualitative and quantitative comparison experiments with three state-of-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos, but also had excellent performance under low frame rate conditions.
2. Proposed Method
2.1. Target Detection
2.2. Dense-Optical-Flow-Trajectory Generation and Voting
2.3. Data-Association Based on Dense-Trajectory Voting
|Algorithm 1: Proposed multiple-target-tracking method.|
3. Experiment Results
3.1. Self-Built Dataset of Aerial Images
3.2. Qualitative Experiments at Different Frame Rates
3.3. Qualitative and Quantitative Comparison Experiments
Conflicts of Interest
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