Robust Visual Tracking Algorithm Based on Siamese Network with Dual Templates
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摘要: 近年来，Siamese网络由于其良好的跟踪精度和较快的跟踪速度，在视觉跟踪领域引起极大关注，但大多数Siamese网络并未考虑模型更新，从而引起跟踪错误。针对这一不足，该文提出一种基于双模板Siamese网络的视觉跟踪算法。首先，保留响应图中响应值稳定的初始帧作为基准模板R，同时使用改进的APCEs模型更新策略确定动态模板T。然后，通过对候选目标区域与2个模板匹配度结果的综合分析，对结果响应图进行融合，以得到更加准确的跟踪结果。在OTB2013和OTB2015数据集上的实验结果表明，与当前5种主流跟踪算法相比，该文算法的跟踪精度和成功率具有明显优势，不仅在尺度变化、平面内旋转、平面外旋转、遮挡、光照变化情况下具有较好的跟踪效果，而且达到了46 帧/s的跟踪速度。Abstract: In recent years, the Siamese networks has drawn great attention in visual tracking community due to its balanced accuracy and speed. However, most Siamese networks model are not updated, which causes tracking errors. In view of this deficiency, an algorithm based on the Siamese network with double templates is proposed. First, the base template R which is the initial frame target with stable response map score and the dynamic template T which is using the improved APCEs model update strategy to determine are kept. Then, the candidate targets region and the two template matching results are analyzed, meanwhile the result response maps are fused, which could ensure more accurate tracking results. The experimental results on the OTB2013 and OTB2015 datasets show that comparing with the 5 current mainstream tracking algorithms, the tracking accuracy and success rate of the proposed algorithm are superior. The proposed algorithm not only displays better tracking effects under the conditions of scale variation, in-plane rotation, out-of-plane rotation, occlusion, and illumination variation, but also achieves real-time tracking by a speed of 46 frames per second.
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Key words:
- Siamese network /
- Object tracking /
- Dual templates /
- Template update
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表 1
$\text{λ} $ 取值对精度、成功率的影响(OTB2015)$\lambda $ 0.50 0.60 0.70 0.80 0.850 0.90 1.00 1.10 成功率 0.447 0.513 0.587 0.603 0.614 0.605 0.585 0.591 精度 0.642 0.697 0.742 0.779 0.793 0.761 0.761 0.774 
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表 2 基于Siamese网络下的双模版跟踪算法
输入: 图像序列: I1, I2, In; 初始目标位置: ${P_0} = ({x_0},{y_0})$, 初始目标大小: ${s_0} = ({w_0},{h_0})$ 输出: 预估目标位置: ${P_{\rm{e}}} = ({x_{\rm{e}}},{y_{\rm{e}}})$, 预估目标大小: ${s_{\rm{e}}} = ({w_{\rm{e}}},{h_{\rm{e}}})$. for t=1, 2,···,n, do: 步骤1 跟踪目标 (1) 以上一帧中心位置${P_{t{\rm{ - 1}}}}$裁剪第t帧中的感兴趣区域ROI，放大为搜索区域； (2) 提取基准模板R，动态模板T和搜索区域的特征； (3) 使用式(4)计算两个模板特征与搜索区域特征的相似性，得到结果响应图,响应图中最高响应点即为预估目标位置。 步骤2 模型更新 (1) 使用式(5)计算跟踪置信度${\rm{APCEs}}$； (2) 计算${F_{{\rm{max}}}}$和${\rm{APCEs}}$的平均值${\rm{m}}{{\rm{F}}_{{\rm{max}}}}$和${\rm{mAPCEs}}$； (3) 如果${F_{{\rm{max}}}}{\rm{ > }}\lambda {\rm{m}}{{\rm{F}}_{{\rm{max}}}}$且${\rm{APCEs}} > \lambda {\rm{mAPCEs}}$，更新动态模板T； Until图像序列的结束。
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表 3 不同属性下算法的跟踪成功率对比结果
算法 SV(64) OPR(63) IPR(51) OCC(49) DEF(44) FM(39) IV(38) BC(31) MB(29) OV(14) LR(9) 本文算法 0.577 0.596 0.595 0.613 0.573 0.607 0.605 0.577 0.633 0.538 0.460 SiameseFC 0.553 0.549 0.579 0.564 0.510 0.569 0.550 0.572 0.525 0.467 0.584 SiameseFC_3S 0.552 0.558 0.557 0.567 0.506 0.568 0.568 0.523 0.550 0.506 0.618 SRDCF 0.561 0.550 0.544 0.569 0.544 0.597 0.613 0.583 0.595 0.460 0.514 Staple 0.525 0.535 0.552 0.561 0.554 0.537 0.598 0.574 0.546 0.481 0.459 MEEM 0.470 0.526 0.529 0.495 0.489 0.542 0.517 0.519 0.557 0.488 0.382
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表 4 不同属性下算法的跟踪精度对比结果
算法 SV(64) OPR(63) IPR(51) OCC(49) DEF(44) FM(39) IV(38) BC(31) MB(29) OV(14) LR(9) 本文算法 0.781 0.796 0.815 0.811 0.804 0.816 0.801 0.770 0.749 0.717 0.878 SiameseFC 0.732 0.744 0.780 0.720 0.690 0.735 0.711 0.748 0.654 0.615 0.805 SiameseFC_3S 0.735 0.757 0.742 0.722 0.690 0.743 0.736 0.690 0.705 0.669 0.900 SRDCF 0.745 0.571 0.745 0.735 0.734 0.769 0.792 0.775 0.767 0.597 0.765 Staple 0.727 0.738 0.770 0.726 0.748 0.697 0.792 0.766 0.708 0.661 0.695 MEEM 0.736 0.795 0.794 0.741 0.754 0.752 0.740 0.746 0.731 0.685 0.808
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表 5 本文算法与5种算法跟踪速度对比
本文算法 SiameseFC SiameseFC_3S SRDCF Staple MEEM Code M+C M+C M+C M+C M+C M+C PlatformFPS GPU46(Y) GPU58(Y) GPU86(Y) GPU5(N) CPU80(Y) CPU10(N)
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