基于动态位置编码和注意力增强的目标跟踪算法
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熊昌镇,郭传玺,王聪
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Target tracking algorithm based on dynamic position encoding and attention enhancement
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Changzhen XIONG,Chuanxi GUO,Cong WANG
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| 表 1 GOT-10K、TrackingNet、UAV123上不同算法的对比 |
| Tab.1 Comparison of different algorithms on GOT-10K, TrackingNet and UAV123 |
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| Trackers | GOT-10K | | TrackingNet | | UAV123 | | AO/% | SR0.50/% | SR0.75/% | AUC/% | PNorm/% | P/% | AUC/% | P/% | | SiamFC[3] | 34.8 | 35.3 | 9.8 | | 57.1 | 66.3 | 53.3 | | 48.5 | 69.3 | | SiamPRN++[21] | 51.7 | 61.6 | 32.5 | 73.3 | 80.0 | 69.4 | 64.2 | 84.0 | | ATOM[22] | 55.6 | 63.4 | 40.2 | 70.3 | 77.1 | 64.8 | 64.3 | — | | Ocean[7] | 61.1 | 72.1 | 47.3 | — | — | — | 62.1 | 82.3 | | DiMP[23] | 61.1 | 71.7 | 49.2 | 74.0 | 80.1 | 68.7 | 65.4 | 85.8 | | KYS[24] | 63.6 | 75.1 | 51.5 | 74.0 | 80.0 | 68.8 | — | — | | DTT[25] | 63.4 | 74.9 | 51.4 | 79.6 | 85.0 | 78.9 | — | — | | PrDiMP[26] | 63.4 | 73.8 | 54.3 | 75.8 | 81.6 | 70.4 | 66.9 | 87.8 | | TrSiam[9] | 66.0 | 76.6 | 57.1 | 78.1 | 82.9 | 72.7 | — | — | | TrDimp[9] | 67.1 | 77.7 | 58.3 | 78.4 | 83.3 | 73.1 | 67.5 | — | | KeepTrack[13] | 68.3 | 79.3 | 61.0 | 78.1 | 83.5 | 73.8 | 69.7 | — | | STARK[27] | 68.8 | 78.1 | 64.1 | 82.0 | 86.9 | — | — | — | | TransT[10] | 72.3 | 82.4 | 68.2 | 81.4 | 86.7 | 80.3 | 69.1 | — | | CTT[28] | — | — | — | 81.4 | 86.4 | — | — | — | | TCTrack[12] | — | — | — | — | — | — | 60.4 | 80.0 | | AiATrack[11] | 69.6 | 77.7 | 63.2 | 82.7 | 87.8 | 80.4 | 69.3 | 90.7 | | ToMP[14] | 73.5 | 85.6 | 66.5 | 81.5 | 86.4 | 78.9 | 65.9 | 85.2 | | MixFormer[15] | 73.2 | 83.2 | 70.2 | 82.6 | 87.7 | 81.2 | 68.7 | 89.5 | | 本研究算法 | 73.9 | 83.3 | 68.6 | 82.7 | 87.7 | 80.8 | 69.3 | 90.5 |
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