生物多样性 ›› 2024, Vol. 32 ›› Issue (3): 23409. DOI: 10.17520/biods.2023409 cstr: 32101.14.biods.2023409
收稿日期:
2023-10-31
接受日期:
2024-01-29
出版日期:
2024-03-20
发布日期:
2024-03-24
通讯作者:
*E-mail: zdhxyh@163.com
基金资助:
Received:
2023-10-31
Accepted:
2024-01-29
Online:
2024-03-20
Published:
2024-03-24
Contact:
*E-mail: zdhxyh@163.com
摘要:
跟踪野生动物可以了解它们的生态习性和种群动态。自动化的、高效准确的目标跟踪算法对于野生动物保护具有重要意义。由于东北虎(Panthera tigris altaica)生存环境复杂, 行动方式隐蔽且具有快速运动的特点, 拍摄到的图像可能存在运动模糊问题, 难以捕获清晰稳定的画面。因此, 监测东北虎种群的难点在于实现自动准确地跟踪东北虎个体。在实际环境中, 由于光照变化、遮挡、相似性干扰等问题的存在, 导致错误跟踪东北虎个体。针对这些问题, 本文提出了一种基于注意力特征融合的孪生网络跟踪框架, 旨在实现对实际复杂场景中东北虎个体的准确跟踪。基于孪生网络的视觉目标跟踪框架将目标跟踪视为相似性学习问题, 本文对传统基于孪生网络的跟踪架构进行改进, 将注意力特征融合嵌入主干网络ResNet50中进行特征提取。为了增强模型对跟踪场景中发生极端尺度变化的东北虎个体的关注度, 本文在注意力特征融合模块中引入了多尺度通道注意力机制, 以适应不同的东北虎个体状态和环境变化。实验结果表明, 本文的方法与当前的先进跟踪器相比取得了更优的跟踪性能, 跟踪成功率和精确度分别达到了72.5%和93.9%, 相比基准跟踪算法分别提高了4.1%和2.3%, 证明本文的跟踪方法可以在复杂的实际场景下为自动高效地监测东北虎提供一种有效的方案。
许群, 谢永华 (2024) 基于注意力机制融合多特征的东北虎个体自动跟踪方法. 生物多样性, 32, 23409. DOI: 10.17520/biods.2023409.
Qun Xu, Yonghua Xie (2024) Automatic individual tracking method of Amur tiger based on attention mechanism fusion of multiple features. Biodiversity Science, 32, 23409. DOI: 10.17520/biods.2023409.
图1 东北虎目标跟踪训练数据集样本示例。(a)无人机拍摄的图像样本; (b)野生东北虎重识别数据集中的图像样本。
Fig. 1 Samples of Amur tiger target tracking training dataset. (a) Image sample taken by unmanned aerial vehicle; (b) Image sample in a benchmark for Amur tiger re-identification in the wild.
视频 Video | 光照变化 Illumination variation | 目标旋转 Object rotation | 部分遮挡 Partial occlusion | 相似干扰 Similarity interference | 目标形变 Object deformation | 尺度变化 Scale variation |
---|---|---|---|---|---|---|
Tiger_01 | √ | √ | - | √ | - | - |
Tiger_02 | - | √ | - | - | √ | √ |
Tiger_03 | - | - | - | √ | - | - |
Tiger_04 | - | √ | - | - | - | - |
Tiger_05 | - | √ | - | - | - | - |
Tiger_06 | √ | √ | - | - | - | - |
Tiger_07 | - | √ | √ | √ | - | - |
Tiger_08 | - | - | - | √ | - | - |
表1 图2各视频序列面对的挑战
Table 1 The challenges of each video sequence in Fig. 2
视频 Video | 光照变化 Illumination variation | 目标旋转 Object rotation | 部分遮挡 Partial occlusion | 相似干扰 Similarity interference | 目标形变 Object deformation | 尺度变化 Scale variation |
---|---|---|---|---|---|---|
Tiger_01 | √ | √ | - | √ | - | - |
Tiger_02 | - | √ | - | - | √ | √ |
Tiger_03 | - | - | - | √ | - | - |
Tiger_04 | - | √ | - | - | - | - |
Tiger_05 | - | √ | - | - | - | - |
Tiger_06 | √ | √ | - | - | - | - |
Tiger_07 | - | √ | √ | √ | - | - |
Tiger_08 | - | - | - | √ | - | - |
图5 基于注意力特征融合的AFF-ResNet50网络的整体结构。C、H、W分别为输入特征图的通道数、高度、宽度; X: ResNet单元中从上一层直接传递过来的原始输入; Y: ResNet单元中残差函数的输出; Z: 融合后的输出特征。
Fig. 5 Overall structure of AFF-ResNet50 network based on attention feature fusion. C, H, W represent the channels, height, and width of the input feature map respectively; X, The original input directly passed from the previous layer in the ResNet block; Y, The output of the residual function in the ResNet block; Z, The fused output feature.
跟踪器 Tracker | 平均像素误差 Average pixel error | 平均重叠率 Average overlap rate |
---|---|---|
本文 This study | 9.016 | 0.737 |
SiamFC | 15.380 | 0.730 |
SiamRPN++ | 9.889 | 0.688 |
SiamCAR | 19.630 | 0.692 |
SiamBAN | 10.495 | 0.694 |
SiamGAT | 11.828 | 0.719 |
表2 本文算法与当前5种先进跟踪方法在8个跟踪视频序列(Tiger_01-Tiger_08)下的平均像素误差与平均重叠率
Table 2 Average pixel error and average overlap rate of this study and five current state-of-the-art tracking methods on eight video sequences (Tiger_01-Tiger_08)
跟踪器 Tracker | 平均像素误差 Average pixel error | 平均重叠率 Average overlap rate |
---|---|---|
本文 This study | 9.016 | 0.737 |
SiamFC | 15.380 | 0.730 |
SiamRPN++ | 9.889 | 0.688 |
SiamCAR | 19.630 | 0.692 |
SiamBAN | 10.495 | 0.694 |
SiamGAT | 11.828 | 0.719 |
图6 本文算法与当前5种先进跟踪方法在视频序列Tiger_02和Tiger_03下的像素误差结果(a)和重叠率结果(b)的曲线图
Fig. 6 A graph of the pixel error results (a) and the overlap rate results (b) of this study and five current state-of-the-art tracking methods on the video sequences Tiger_02 and Tiger_03
图7 本文算法与当前5种先进跟踪方法在东北虎目标跟踪测试数据集上的跟踪成功率与精确度对比
Fig. 7 Comparison of tracking success rate and precision of this study and five current state-of-the-art tracking methods on the Amur tiger target tracking test dataset
跟踪器 Tracker | 光照变化 Illumination variation | 目标旋转 Object rotation | 部分遮挡 Partial occlusion | 相似干扰 Similarity interference | 目标形变 Object deformation | 尺度变化 Scale variation |
---|---|---|---|---|---|---|
本文 This Study | 0.719 | 0.723 | 0.728 | 0.711 | 0.740 | 0.755 |
SiamFC | 0.794 | 0.694 | 0.750 | 0.673 | 0.734 | 0.743 |
SiamRPN++ | 0.663 | 0.651 | 0.728 | 0.649 | 0.728 | 0.711 |
SiamCAR | 0.694 | 0.680 | 0.712 | 0.680 | 0.709 | 0.719 |
SiamBAN | 0.672 | 0.672 | 0.722 | 0.656 | 0.722 | 0.718 |
SiamGAT | 0.681 | 0.683 | 0.729 | 0.728 | 0.728 | 0.744 |
表3 本文算法与当前5种先进跟踪方法在东北虎目标跟踪测试数据集中6种挑战的成功率
Table 3 Tracking success rate of this study and five current state-of-the-art tracking methods on six types of challenges in the Amur tiger target tracking test dataset
跟踪器 Tracker | 光照变化 Illumination variation | 目标旋转 Object rotation | 部分遮挡 Partial occlusion | 相似干扰 Similarity interference | 目标形变 Object deformation | 尺度变化 Scale variation |
---|---|---|---|---|---|---|
本文 This Study | 0.719 | 0.723 | 0.728 | 0.711 | 0.740 | 0.755 |
SiamFC | 0.794 | 0.694 | 0.750 | 0.673 | 0.734 | 0.743 |
SiamRPN++ | 0.663 | 0.651 | 0.728 | 0.649 | 0.728 | 0.711 |
SiamCAR | 0.694 | 0.680 | 0.712 | 0.680 | 0.709 | 0.719 |
SiamBAN | 0.672 | 0.672 | 0.722 | 0.656 | 0.722 | 0.718 |
SiamGAT | 0.681 | 0.683 | 0.729 | 0.728 | 0.728 | 0.744 |
跟踪器 Tracker | 光照变化 Illumination variation | 目标旋转 Object rotation | 部分遮挡 Partial occlusion | 相似干扰 Similarity interference | 目标形变 Object deformation | 尺度变化 Scale variation |
---|---|---|---|---|---|---|
本文 This study | 0.972 | 0.970 | 0.887 | 0.849 | 0.904 | 0.982 |
SiamFC | 0.864 | 0.748 | 0.724 | 0.563 | 0.745 | 0.916 |
SiamRPN++ | 0.964 | 0.959 | 0.880 | 0.840 | 0.897 | 0.982 |
SiamCAR | 0.767 | 0.786 | 0.753 | 0.776 | 0.769 | 0.894 |
SiamBAN | 0.924 | 0.944 | 0.878 | 0.829 | 0.892 | 0.973 |
SiamGAT | 0.937 | 0.841 | 0.756 | 0.853 | 0.773 | 0.918 |
表4 本文算法与当前5种先进跟踪方法在东北虎目标跟踪测试数据集中6种挑战的精确度
Table 4 Tracking precision of this study and five current state-of-the-art tracking methods on six types of challenges in the Amur tiger target tracking test dataset
跟踪器 Tracker | 光照变化 Illumination variation | 目标旋转 Object rotation | 部分遮挡 Partial occlusion | 相似干扰 Similarity interference | 目标形变 Object deformation | 尺度变化 Scale variation |
---|---|---|---|---|---|---|
本文 This study | 0.972 | 0.970 | 0.887 | 0.849 | 0.904 | 0.982 |
SiamFC | 0.864 | 0.748 | 0.724 | 0.563 | 0.745 | 0.916 |
SiamRPN++ | 0.964 | 0.959 | 0.880 | 0.840 | 0.897 | 0.982 |
SiamCAR | 0.767 | 0.786 | 0.753 | 0.776 | 0.769 | 0.894 |
SiamBAN | 0.924 | 0.944 | 0.878 | 0.829 | 0.892 | 0.973 |
SiamGAT | 0.937 | 0.841 | 0.756 | 0.853 | 0.773 | 0.918 |
图8 在6种挑战下的东北虎跟踪结果定性比较。a-e分别表示光照变化、目标旋转、部分遮挡、相似干扰、目标形变和尺度变化挑战。
Fig. 8 Qualitative comparison of tracking results of Amur tigers under six types of challenges. a-e represent illumination variation, object rotation, partial occlusion, similarity interference, object deformation, and scale variation challenges, respectively.
模型 Model | 成功率 Success rate (%) | 精确度 Precision (%) |
---|---|---|
SiamBAN | 68.4 | 91.6 |
SiamBAN + 多尺度通道注意力机制 SiamBAN + Multi-scale channel attention module (MS-CAM) | 67.0 | 93.5 |
SiamBAN + 注意力特征融合 SiamBAN + Attentional feature fusion (AFF) | 72.5 | 93.9 |
SiamBAN + 特征金字塔网络 SiamBAN + Feature pyramid networks (FPN) | 63.6 | 94.1 |
表5 SiamBAN以及其采用不同特征融合方法的跟踪成功率与精确度对比
Table 5 Comparison of tracking success rate and precision of SiamBAN with various feature fusion methods
模型 Model | 成功率 Success rate (%) | 精确度 Precision (%) |
---|---|---|
SiamBAN | 68.4 | 91.6 |
SiamBAN + 多尺度通道注意力机制 SiamBAN + Multi-scale channel attention module (MS-CAM) | 67.0 | 93.5 |
SiamBAN + 注意力特征融合 SiamBAN + Attentional feature fusion (AFF) | 72.5 | 93.9 |
SiamBAN + 特征金字塔网络 SiamBAN + Feature pyramid networks (FPN) | 63.6 | 94.1 |
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