Moving ship detection based on visual saliency for video satellite

Li, Haichao; Man, Yiyun

doi:10.1109/igarss.2016.7729316

Cited by 21 publications

(10 citation statements)

References 7 publications

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“…In addition, note that the moving objects in satellite videos include vehicles, airplanes, rockets, and ships. Compared with moving target tracking in ordinary videos, realtime object tracking in satellite videos should overcome three main challenges as follows [51,52].…”

Section: Introductionmentioning

confidence: 99%

“…Due to these above challenges and the increasing demand for MOT in the field of remote sensing, up until now, several moving object detection/tracking algorithms have been carefully designed for MOT in satellite videos [51][52][53][54][55]. Lei and Guo [53] propose a road masking and Gaussian mixture method to achieve multiple object detection and tracking of the remote sensing video satellite.…”

Section: Introductionmentioning

confidence: 99%

“…In [51], a fusion tracker is introduced where the kernel correlation filter and the three-framedifference method are synthesized for satellite videos to improve the performance of the tracker. Li and Man [52] put forward an optical-flow-based detection algorithm with video attention saliency for the moving ships in the satellite videos, where the Gabor filter is utilized to extract the texture feature, and the registration of the sea-scene images can be avoided. Liu et al [54] present a kernelized correlation filter where multifeature fusion and motion trajectory compensation are employed for satellite videos to mitigate the tracking drifts.…”

Section: Introductionmentioning

confidence: 99%

“…However, the above trackers [51,52,54,55] generally rely on the original pixel information (such as HSV or HOG), which ignores the color information. In fact, several satellite videos contain some color-name (CN) features.…”

Section: Introductionmentioning

confidence: 99%

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Moving Object Tracking in Satellite Videos by Kernelized Correlation Filter Based on Color-Name Features and Kalman Prediction

Pei

2022

Wireless Communications and Mobile Computing

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This paper studies moving object tracking in satellite videos. For the satellite videos, the object size in the images may be small, the object may be partly occluded, and the image may contain an area resembling dense objects. To handle the above problems, this paper puts forward a kernelized correlation filter based on the color-name feature and Kalman prediction. The original image is mapped to the color-name feature space so that the tracker can process the image with multichannel color features. The Kalman filter is used to predict the moving object position in the tracking process, and the detection area is determined according to the predicted position. The Kalman filter is updated with the detection results to improve the tracking accuracy. The proposed algorithm is tested on Jilin-1 datasets. Compared with the other seven tracking algorithms, the experiment results show that the proposed algorithm has stronger robustness for several complex situations such as rapid target motion and similar object interference. Besides, it is also shown that the proposed algorithm can prevent the problem of tracking failure when the moving object is partially occluded.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moving Object Tracking in Satellite Videos by Kernelized Correlation Filter Based on Color-Name Features and Kalman Prediction

Pei

2022

Wireless Communications and Mobile Computing

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“…A satellite video is a newly developed earth observation satellite, which can capture continuous image sequences in a long time (Xiao, Wang, Wang, & Ren, 2018). It has received attention in many real-world tasks, such as motion analysis (He, Yi, Cheung, You, & Tang, 2017), moving ship surveillance (Li & Man, 2016), traffic monitoring(Caro-Gutierrez, Bravo-Zanoguera, & Gonzá lez-Navarro, 2017), (Patel & Mishra, 2013). In general, the size of a frame in the satellite video can be up to 3840 × 2160 pixels .Yet the size of moving objects in satellite video usually can be very small because of the long distance from the camera, consequently the information we can obtain is limited comparing with the background.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tracking in Satellite Videos via Joint Discrimination and Pose Estimation

Chen

Sui

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Object tracking has gained much attention in the field of computer vision and intelligent traffic analysis. Satellite videos are more suitable for long-distance tracking comparing to the road traffic videos. However, most of the state-of-the-art methods produce poor results when applied to satellite videos, due to low resolution of the small target and interference from similar background in satellite videos. In this paper, we present an improved Discriminative Correlation Filter based approach specifically tailored for small objects tracking in satellite videos through applying spatial weight in the filter and estimating the pose by Kalman filter. First, a spatial mask is introduced to encourage the final filter to give different contributions depending on the spatial distance. Furthermore, Kalman filter is incorporated into the approach with the aim of predicting the position when the target run into the large area similar background region. Finally, an efficient strategy for combining the improved DCF tracker and pose estimation is proposed. Experimental results on three satellite videos describing the traffic conditions of three cities demonstrate that the proposed approach can effectively track the targets even though the targets are similar to the background region in a period of time. Compare with other state-of-the-art methods, the final accuracies and speeds of our method achieve the best.</p>

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