High-resolution satellite videos are playing an increasingly important role as a means of monitoring dynamic ground targets. Currently, satellite video object tracking is confronted with small size, partial occlusion, residual shadow, poor target-background discriminability, shape deformation and poor general field illumination challenges. To solve the above problems, we design a ThickSiam framework, which consists of a Thickened Residual Block Siamese Network (TRBS-Net) and an Improved Kalman Filter (IKF) module. The TRBSNet is stacked by well-designed thickened residual blocks and thickened maxpooling residual block, while the IKF module is designed to simultaneously correct the trajectory and size of the target. We propose an N-frame-convergence mechanism to deal with the burn-in period problem existed in IKF module and weightedly combine the results of TRBS-Net and IKF modules. We also construct a testing dataset suitable for satellite video object tracking task. We conducted ablation experiments on the constructed dataset and compared the proposed ThickSiam framework with other nineteen state-of-the-art trackers including different features and backbones. The comparison results show that our ThickSiam tracker obtained a precision value of 0.991 and a success value of 0.755 with a frames per second (FPS) value of 56.849 implemented on a single NVIDIA GTX1070Ti GPU.
We modularly verified the effectiveness of the overall framework based on three training schemes: training with individual COCO dataset, training with individual DIOR dataset, and training with joint COCO and DIOR datasets. We stacked the original residual block and down-sampling residual block to assemble the baseline network. The precision plots and success plots about the comparison results of different methods with three training schemes are shown in the figure below.
We compared the ThickSiam Framework with other 19 state-of-the-art trackers including CF-based and DL-based methods with different features and backbones on our constructed testing dataset. They are MOSSE, CSK, KCF, CN, DSST, Staple, SiamFC, DCFNet, ECO, STRCF, ATOM, DiMP, SiamFC+, SiamRPN+, SiamRPN++, SiamFC++ and ID-DSN. The comparison results are shown in the following Table.
| Trackers | Methods | Features | Backbones | CUDA | Precision | Success | FPS |
|---|---|---|---|---|---|---|---|
| MOSSE(2010) | CF-based | Grayscale Intensity | - | - | 0.745 | 0.48 | 4.964 |
| CSK(2012) | CF-based | Grayscale Intensity | - | - | 0.755 | 0.512 | 5.478 |
| KCF(2014) | CF-based | HOG | - | - | 0.851 | 0.634 | 18.21 |
| CN(2014) | CF-based | Color Table | - | - | 0.859 | 0.609 | 8.763 |
| DSST(2014) | CF-based | HOG | - | - | 0.782 | 0.596 | 9.72 |
| Staple(2016) | CF-based | HOG+Color Histogram | - | - | 0.776 | 0.58 | 10.887 |
| SiamFC | DL-based | CNN Features | AlexNet | √ | 0.902 | 0.663 | 127.174 |
| DCFNet(2017) | CF-based | CNN Features | conv1 from VGG | √ | 0.833 | 0.644 | 12.4 |
| ECO(2017) | CF-based | CNN Features | ResNet18 with vgg-m conv1 layer | - | 0.856 | 0.645 | 3.998 |
| STRCF(2018) | CF-based | HOG+Color Table | - | - | 0.795 | 0.557 | 7.498 |
| ATOM(2019) | DL-based | CNN Features | ResNet18 | √ | 0.852 | 0.622 | 10.771 |
| DiMP(2019) | DL-based | CNN Features | ResNet18 | √ | 0.717 | 0.545 | 12.697 |
| DiMP(2019) | DL-based | CNN Features | ResNet50 | √ | 0.747 | 0.597 | 11.239 |
| SiamFC+(2019) | DL-based | CNN Features | ResNet22 | √ | 0.839 | 0.652 | 59.333 |
| SIamRPN+(2019) | DL-based | CNN Features | ResNet22 | √ | 0.878 | 0.618 | 114.867 |
| SiamRPN++(2019) | DL-based | CNN Features | AlexNet | √ | 0.883 | 0.656 | 144.783 |
| SiamRPN++(2019) | DL-based | CNN Features | ResNet50 | √ | 0.828 | 0.655 | 31.617 |
| SiamFC++(2020) | DL-based | CNN Features | AlexNet | √ | 0.925 | 0.699 | 139.828 |
| ID-DSN（2021） | DL-based | CNN Features | ResNet50 | √ | 0.933 | 0.718 | 31.167 |
| ThickSiam (ours, TRBS-Net) | DL-based | CNN Features | TRB+TMRB | √ | 0.959 | 0.721 | 56.758 |
| ThickSiam (ours, TRBS-Net+IKF) | DL-based | CNN Features | TRB+TMRB | √ | 0.991 | 0.755 | 56.758 |
The precision plots and success plots of the ablation experiments with the state-of-the-art trackers are shown in the figure below.
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Licensed under an MIT license.