Distinguishing the target from the background, judging target occlusion, and real-time processing are the problems that the visual tracking algorithm still needs to solve. Color information and position information of the target block are fused as new features to track the target under the framework of particle filtering. First, the hues, saturation, value space, and color integral graph of the image are constructed. The vector representation of the target is obtained on the color integral image by sparse matrix. Then, candidate particles are produced by a particle filter and the sampling mode of particles is adjusted by a uniform acceleration model. The difference of particles reflects the position and scale change of the target. Finally, the candidate with the smallest eigenvector projection error is taken as the tracking target and the feature template is updated based on the tracking results. The presented algorithm can be used to track a single target in the color image sequence and has some robustness to the scale change, occlusion, and morphological change of the target. Experiment results on public datasets show that the proposed algorithm performs favorably in both speed and tracking effect when compared with other conventional trackers.
For the problems of large viewpoint variation, heavy distortion and small overlap area in UAV images registration, this paper proposes a density analysis based method to remove mismatches in putative feature correspondences. Our method uses intra-cluster topological constraints for mismatch filtering, which is based on a density-based hierarchical clustering algorithm. Compared with other methods that perform mismatch filtering based on neighborhood topological relationship, our method is more robust to viewpoint changes both in horizontal and vertical directions. The algorithm in this paper uses a coarse-to-fine strategy, which starts with establishing putative feature correspondences based on local descriptors, such as SIFT, ORB, etc. After that we focus on removing outliers by clustering these feature points and verifying topology consistency of the clusters in different images. We view the feature point matching problem as a correspondence problem of the same visual model in two images, and clustering the feature points based on density can approximate the separation of multiple visual patterns. We tested our algorithm on a UAV image dataset which includes several pairs of images and their ground truth. These image pairs contain viewpoint changes in horizontal, vertical and their mixture which produce problems of low overlap, image distortion and severe outliers. Experiments demonstrate that our method significantly outperforms the state-of-the-art in terms of matching precision.
As the application of UAVs in military and civilian fields becomes more and more widespread, the detection of UAVs in the low-altitude range has also become an important research direction. Compared with radar and visible light detection, infrared technology has become the major UAV detection method with its advantages of all-weather and long range. Most of the current infrared target detection methods are based on convolutional neural networks (CNN), which achieve target detection through feature extraction and feature classification. The performance of all such detection algorithms is highly dependent on their training set. A data set with a large number of samples and wide coverage tends to train a more robust and accurate detector. So, to obtain better detection effects, we perform data augmentation on the infrared UAV dataset by adversarial generative network (GAN). First, we extract the targets from the training set and train a GAN network, using its generator to obtain many new targets which are different from the training set samples, then we randomly extend these targets to the original dataset, and finally we retrain the detectors using the new dataset to achieve better detection. We created an infrared UAV image dataset for our experiments, with only a single target on each image. After data augmentation, multiple UAV targets are randomly generated. The experiments demonstrate that the new dataset trains the model with better detection results. And the GAN data augmentation can be combined with many advanced detectors to make a large improvement in detection.
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