Graduation Design - Fine-grained Feature Based TIR Tracking
PixelNet embedded SiamRPN++ tracker using ResNet-50 as backbone
In this project, the famous Multi-Head Attention framework is used to build my fine-grained feature extraction network. I combine three Global Context Block with Multi-Head Attention framework and integrate it into SiamRPN++ tracker. The result is satisfying.
But unfortunately when I got this pleasing model, I have done my graduation design and got my Bachelor degree 🙃
模型结构如下：
主要使用框架：PySOT中的SiamRPN++追踪器
课题中增加的代码有：
- 探究RGB图像与热红外图像特性对比时使用的代码
- 骨干网络中嵌入PixelNet时使用的与pixelnet和resnet相关的代码
- 添加对热红外图像训练集TIR的支持（修改config代码）
- 添加热红外图像训练集TIR及其预处理代码
- 添加对热红外图像测试集PTB-TIR的支持（修改init代码，增加预处理代码）
- 添加热红外图像测试集PTB-TIR及其预处理代码
在离校前得到的结果：
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SiamRPN++追踪器在RGB图像与TIR图像数据集中的表现
Dataset Accuracy Robustness Loss EAO VOT2019 0.594 0.467 93 0.287 PTB-TIR 0.404 0.194 51 0.308 -
PixelNet两种嵌入方式的比较
Model Accuracy Robustness Loss EAO FPS PixelNet-2b-a 0.413 0.362 95 0.247 53.6 PixelNet-2b-b 0.408 0.331 87 0.260 62.4 -
PixelNet（PixelNet-3b）与原模型的比较
Model Accuracy Robustness Loss EAO FPS PixelNet 0.419 0.305 80 0.275 67.9 Original 0.428 0.350 92 0.264 69.3 -
PixelNet中AttentionBlock的个数的比较
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10个epoch时
AttnBlk No. Accuracy Robustness Loss EAO FPS PixelNet-1b 0.378 0.312 82 0.245 72.5 PixelNet-2b 0.363 0.354 93 0.225 70.3 PixelNet-3b 0.369 0.312 82 0.240 68.4 PixelNet-4b 0.357 0.343 90 0.220 66.4 -
19个epoch时
AttnBlk No. Accuracy Robustness Loss EAO FPS PixelNet-1b 0.418 0.339 89 0.259 72.2 PixelNet-2b 0.408 0.331 87 0.260 62.4 PixelNet-3b 0.419 0.305 80 0.275 67.9 PixelNet-4b 0.399 0.350 92 0.240 64.4
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