|
| | Algorithm | Advantages | Disadvantages |
|
| DBT | Pipeline filtering method [7] | Simple process; easy for engineering implementation | Failure when the position of target does not change and low SNR |
|
| TBD | 3D matched filtering method [1] | High detection performance; able to detect multiple trajectories simultaneously | Only applied to the case of known speed and direction |
| Project transformation method [2] | Effectively reducing the amount of data and storage during the 3D search and detection process | Not adapted to the target detection with low SNR and large inter frame displacement |
| Dynamic programming method [3] | Able to detect the target trajectory of points in linear motion in the case of low SNR | Requiring a priori knowledge of the velocity window parameters |
| Multistage hypothesis testing method [4] | Able to detect multiple targets in linear motion simultaneously | Only adapted to the scene of targets in local uniform linear motion |
| High-order correlation method [5] | Able to detect the linear or curve trajectory, requiring no prior knowledge | Detection results being affected greatly by order |
|
| Latest algorithms | Visual saliency [8, 9] | Able to quickly locate the region of interest | Only adapted to scenes with big differences between the target and the background, and more obvious characteristics for the target |
| Sparse representation [10–13] | Effectively enhancing the sparse feature difference between the target and the background and improving the detection accuracy through training | Only adapted to stable or slowly changing background, and scenes with high SNR |
| The proposed method | Able to effectively detect scenes with low SNR (SNR < 3 dB) | Requiring large amount of computation |
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