Infrared dim and small target detection plays an important role in different applications, and many infrared dim and small target detection methods have been proposed. However, most existing methods are based on single camera. Although they have achieved good performance, most of them have poor performance in complex scenes with dim and small targets. Based on this, we introduce the array camera, which can provide more view information, so as to obtain better performance in the complex scene of dim and small targets. Furthermore, in order to further improve the confidence of detection rate, we introduce a probability estimation module into our method. Specially, the array camera provides more target position information through the view-correlation frames. And the probability estimation module is introduced to fuse the information of each view. Extensive experiments on different scenes demonstrate that our method achieves better performance in dim and small target detection, and obtains higher confidence of detection rate and lower false alarm.
Multi-object tracking in satellite videos has been widely used in civilian and military fields. Among them, the tracking of vehicles has important applications in the field of traffic monitoring. However, the tracking of vehicles in satellite videos still remains challenging and unsolved due to the extremely small size and the lack of appearance and geometric features. In this paper, we propose an improved SORT to tackle the tracking of vehicles in satellite videos by introducing C3D to CenterNet to improve the detection performance and promote the overall tracking performance. Specifically, we use C3D as the backbone of CenterNet to extract spatio-temporal information and use a 3D channel attention mechanism to fuse the information extracted by C3D to improve the detection performance, thereby improving the tracking results. The qualitative and quantitative results of experiments on videos of Jilin-1 satellite constellation show that our method can efficiently improve the tracking performance of vehicles in satellite videos.
In recent years, with the development of earth observation technology, satellite video can use optical sensors to obtain continuous images from mobile satellite platforms, providing new data for the detection and tracking of large-scale moving targets. At present, moving target detection algorithms have been widely used in ground surveillance video. However, there are many challenges in applying existing target detection algorithms directly to satellite video due to the low resolution of satellite video, non-appearance texture feature of small target, low signal-to-noise ratio, and nonstationary camera platforms, etc. Therefore, we propose a new satellite video moving target detection and tracking framework for this new type of computer vision task. First, we utilize a tensor data structure to exploit the inner spatial and temporal correlation to extract region of interest for target movement. Then, we designed a recognition strategy based on multi-morphology and motion cues to further identify the correct moving targets from the existing noise. Finally, we associate the target detection results of each frame to achieve multi-target tracking. We manually annotated the video data of Jilin-1 satellite, tested the algorithm under different evaluation criteria, and compared the test results with the most advanced benchmark, which proved the advantages of our framework over the benchmark. In addition, the data set can be downloaded from https://github.com/QingyongHu/VISO.
Recent years have witnessed a strong renewal of interest in light field cameras, as they can capture rich angular information within one snapshot. As a representative application of light field cameras, refocusing can change the in-focus region of images so that objects lying on a specified plane are in focus, whereas objects lying off this plane are blurred. The existing refocusing methods can only project images onto focal planes. In this paper, we proposed a reprojetion-based method to refocus the images captured by camera arrays onto arbitrary focal surfaces, rather than only planes. Combining the camera imaging model and the equation of the focal surface, we can reproject the images onto arbitrary focal surface. We can change the focal surface by changing the equation of the focal surface. Experiments on real-world scenes (captured by our self-developed light field devices) demonstrate the validity of the proposed method.
Light field cameras have drawn much attention due to the advantage of post-capture adjustments such as refocusing after exposure. The depth of field in refocused images is always shallow because of the large equivalent aperture. As a result, a large number of multi-focus images are obtained and an all-in-focus image is demanded. Consider that most multi-focus image fusion algorithms do not particularly aim at large numbers of source images and traditional DWT-based fusion approach has serious problems in dealing with lots of multi-focus images, causing color distortion and ringing effect. To solve this problem, this paper proposes an efficient multi-focus image fusion method based on stationary wavelet transform (SWT), which can deal with a large quantity of multi-focus images with shallow depth of fields. We compare SWT-based approach with DWT-based approach on various occasions. And the results demonstrate that the proposed method performs much better both visually and quantitatively.
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