Caffe Siamese Track.

最新推荐文章于 2024-07-16 17:06:34 发布
原创 最新推荐文章于 2024-07-16 17:06:34 发布 · 213 阅读 · 0 ·
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博客提及了 Train 网络和 Test 网络，推测是关于网络的训练与测试相关内容，属于信息技术领域。

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Train 网络 

name: "Face_Track"
layer {
  name: "data_face"
  type: "Data"
  top: "data_face"
  include {
    phase: TRAIN
  }
  transform_param {
		mean_value: 104
		mean_value: 117
		mean_value: 124
		scale: 0.0078125
  }
  data_param {
    source: "lmdb_face/train_data_npd"
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "data_face"
  type: "Data"
  top: "data_face"
  include {
    phase: TEST
  }
  transform_param {
		mean_value: 104
		mean_value: 117
		mean_value: 124
		scale: 0.0078125
  }
  data_param {
    source: "lmdb_face/test_data_npd"
    batch_size: 64
    backend: LMDB
  }
}



layer {
  name: "data_face_around"
  type: "Data"
  top: "data_face_around"
  include {
    phase: TRAIN
  }
  transform_param {
		mean_value: 104
		mean_value: 117
		mean_value: 124
		scale: 0.0078125
  }
  data_param {
    source: "lmdb_face_around/train_data_npd"
    batch_size: 64
    backend: LMDB
  }
}

layer {
  name: "data_around_label"
  type: "Data"
  top: "data_around_label"
  include {
    phase: TRAIN
  }
  data_param {
    source: "lmdb_face_around/train_label_npd"
    batch_size: 64
    backend: LMDB
  }
}

layer {
  name: "data_face_around"
  type: "Data"
  top: "data_face_around"
  include {
    phase: TEST
  }
  transform_param {
		mean_value: 104
		mean_value: 117
		mean_value: 124
		scale: 0.0078125
  }
  data_param {
    source: "lmdb_face_around/test_data_npd"
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "data_around_label"
  type: "Data"
  top: "data_around_label"
  include {
    phase: TEST
  }
  data_param {
    source: "lmdb_face_around/test_label_npd"
    batch_size: 64
    backend: LMDB
  }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data_face"
  top: "conv1"
  param {
	name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv1_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    stride: 2
	pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
  param {
	name: "prelu1_p"
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
	name: "conv2_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv2_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "prelu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
  param {
	name: "prelu2_p"
  }
}

layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
	name: "conv3_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv3_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 32
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
  param {
	name: "prelu3_p"
  }
}

layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
	name: "conv4_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv4_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 64
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu4"
  type: "PReLU"
  bottom: "conv4"
  top: "conv4"
  param {
	name: "prelu4_p"
  }
}

layer {
  name: "con5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
	name: "conv5_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv5_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}






layer {
  name: "conv1_P"
  type: "Convolution"
  bottom: "data_face_around"
  top: "conv1_P"
  param {
	name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv1_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    stride: 2
	pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu1_p"
  type: "PReLU"
  bottom: "conv1_P"
  top: "conv1_P"
  param {
	name: "prelu1_p"
  }
}

layer {
  name: "conv2_P"
  type: "Convolution"
  bottom: "conv1_P"
  top: "conv2_P"
  param {
	name: "conv2_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv2_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "prelu2_p"
  type: "PReLU"
  bottom: "conv2_P"
  top: "conv2_P"
  param {
	name: "prelu2_p"
  }
}

layer {
  name: "conv3_p"
  type: "Convolution"
  bottom: "conv2_P"
  top: "conv3_p"
  param {
	name: "conv3_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv3_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 32
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu3_p"
  type: "PReLU"
  bottom: "conv3_p"
  top: "conv3_p"
  param {
	name: "prelu3_p"
  }
}

layer {
  name: "conv4_p"
  type: "Convolution"
  bottom: "conv3_p"
  top: "conv4_p"
  param {
	name: "conv4_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv4_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 64
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu4_p"
  type: "PReLU"
  bottom: "conv4_p"
  top: "conv4_p"
  param {
	name: "prelu4_p"
  }
}

layer {
  name: "con5_p"
  type: "Convolution"
  bottom: "conv4_p"
  top: "con5_p"
  param {
	name: "conv5_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv5_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 1
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
	name: "tile_h"
	type: "Tile"
	bottom: "conv5"
	top: "tile_h"
	tile_param{
	  axis :2
	  tiles :8
	}
}
layer {
	name: "tile_v"
	type: "Tile"
	bottom: "tile_h"
	top: "tile_v"
	tile_param{
	  axis :3
	  tiles :8
	}
}

layer {
  name: "eltwise"
  type: "Eltwise"
  bottom: "tile_v"
  bottom: "con5_p"
  top: "eltwise"
  eltwise_param{
     operation:PROD
  }
}


layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "eltwise"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu_fc1"
  type: "PReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 4
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "AbsoluteLoss"
  #type:"EuclideanLoss"
  bottom: "ip2"
  bottom: "data_around_label"
  top: "loss"
}

layer {
  name: "accuracy"
  type: "AbsoluteLoss"
  #type:"EuclideanLoss"
  bottom: "ip2"
  bottom: "data_around_label"
  top: "accuracy"
  include {
    phase: TEST
  }
}

Test 网络

name: "Face_Track"
layer
{  
  name: "data"  
  type: "MemoryData"
  top: "data_face"
  top: "label1"
  memory_data_param   
  {
    batch_size: 1
    channels: 3
    height: 48
    width: 48
  }
  transform_param {
		mean_value: 104
		mean_value: 117
		mean_value: 124
		scale: 0.0078125
  }
}

layer
{  
  name: "data2"  
  type: "MemoryData"
  top: "data_face_around"
  top: "label2"
  memory_data_param   
  {
    batch_size: 1
    channels: 3
    height: 120
    width: 120
  }
  transform_param {
		mean_value: 104
		mean_value: 117
		mean_value: 124
		scale: 0.0078125
  }
}


layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data_face"
  top: "conv1"
  param {
	name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv1_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    stride: 2
	pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
  param {
	name: "prelu1_p"
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
	name: "conv2_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv2_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "prelu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
  param {
	name: "prelu2_p"
  }
}

layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
	name: "conv3_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv3_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 32
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
  param {
	name: "prelu3_p"
  }
}

layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
	name: "conv4_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv4_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 64
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu4"
  type: "PReLU"
  bottom: "conv4"
  top: "conv4"
  param {
	name: "prelu4_p"
  }
}

layer {
  name: "con5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
	name: "conv5_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv5_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}






layer {
  name: "conv1_P"
  type: "Convolution"
  bottom: "data_face_around"
  top: "conv1_P"
  param {
	name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv1_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    stride: 2
	pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu1_p"
  type: "PReLU"
  bottom: "conv1_P"
  top: "conv1_P"
  param {
	name: "prelu1_p"
  }
}

layer {
  name: "conv2_P"
  type: "Convolution"
  bottom: "conv1_P"
  top: "conv2_P"
  param {
	name: "conv2_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv2_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "prelu2_p"
  type: "PReLU"
  bottom: "conv2_P"
  top: "conv2_P"
  param {
	name: "prelu2_p"
  }
}

layer {
  name: "conv3_p"
  type: "Convolution"
  bottom: "conv2_P"
  top: "conv3_p"
  param {
	name: "conv3_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv3_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 32
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu3_p"
  type: "PReLU"
  bottom: "conv3_p"
  top: "conv3_p"
  param {
	name: "prelu3_p"
  }
}

layer {
  name: "conv4_p"
  type: "Convolution"
  bottom: "conv3_p"
  top: "conv4_p"
  param {
	name: "conv4_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv4_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 64
	kernel_size: 3
    stride: 2
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu4_p"
  type: "PReLU"
  bottom: "conv4_p"
  top: "conv4_p"
  param {
	name: "prelu4_p"
  }
}

layer {
  name: "con5_p"
  type: "Convolution"
  bottom: "conv4_p"
  top: "con5_p"
  param {
	name: "conv5_w"
    lr_mult: 1
    decay_mult: 1
  }
  param {
	name: "conv5_b"
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 1
	pad:1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
	name: "tile_h"
	type: "Tile"
	bottom: "conv5"
	top: "tile_h"
	tile_param{
	  axis :2
	  tiles :8
	}
}
layer {
	name: "tile_v"
	type: "Tile"
	bottom: "tile_h"
	top: "tile_v"
	tile_param{
	  axis :3
	  tiles :8
	}
}

layer {
  name: "eltwise"
  type: "Eltwise"
  bottom: "tile_v"
  bottom: "con5_p"
  top: "eltwise"
  eltwise_param{
     operation:PROD
  }
}


layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "eltwise"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu_fc1"
  type: "PReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 4
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

 

Siamese Instance Search for Tracking 阅读笔记
aiqiu_gogogo的博客
02-01 3195
这是一篇CVPR2016的用DL思想解决Tracking的文章。文章思想简单易懂，但是效果却很好，在OTB50上获得了 state-of-the-art。 核心思想： 通过Siamese网络学习一个匹配函数。在得到第一帧目标信息后，接下来的所有帧采样后都和第一帧目标进行匹配度计算，得分最高的即为目标。（ps：这里，算法设计的核心思想在于永远和第一帧的目标进行匹配度计算，这样就会有一下几个好处：
Caffe学习笔记（三）——Matlab接口
hong__fang的专栏
07-31 1万+
本文主要介绍：Windows下Caffe框架的Matlab接口，即Matlab如何调用Caffe框架中的函数，进行分类、提取特征以及训练。 1 官网说明 官网：点击打开链接 2 注意事项 待续。。。
Siamese Tracking with Lingual Object Constraints
weixin_43390051的博客
03-29 511
Siamese Tracking with Lingual Object Constraints阅读笔记摘要Related WorkVQATracking标准Architechturepre-processingtrackerWord EmbeddingsFrameworkSiamCT-DFG 摘要 本文将tracking任务增加了lingual限制，即给定一串文本，tracker根据文本的信息来追踪特定的目标。 与另一篇将跟踪与文本相结合的文章Tracking by Natural Language Sp
caffe学习笔记初识siamese
zc的csdn
04-02 948
对mnist数据集进行 训练：使用siamese训练： 1 siamese数据的输入是？leveldb 与hdf5 与lmdb 本次实验是使用mnist使用mnist数据集，转换为leveldb格式2 siamenes网络结构(1)siamese 网络单结构3 siamese 如何训练在ubuntu1首先获取data下获取 minst数据2转成leveldb格式文件 ./data/mnist/g...
SiamSE: Scale Equivariance Improves Siamese Tracking论文和代码解读
Soonki的博客
06-26 293
结果依赖于所选的规范，但是对于各个规范都是等价的，例如对向量场进行变换，输出的向量场也会产生对应变换；对Kg的质量场进行变换，则以斤为单位的质量场也会变换。也就是说template和search进行相似度计算后，数值最大的地方是目标所在地，而数值非常小的是背景(已经经过了softmax)。是标准做法，这样可以有效保存图像的空间信息，但是补零的话，会让卷积追踪器的定位属性裂化。当且仅当在规范卷积的情况下，追踪器对于规范群的变换也是等变的。，这种特性和相似特征的正交性是非常符合的。是规范等变卷积层构成的，
单目标跟踪：模型测试
あずにゃん梓喵的博客
07-17 1816
日萌社 人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新） 1.6 test 学习目标： 了解网络测试的过程 能够实现网络的训练代码编写 网络的测试test主要在tools中test.py中实现，该文件中包含的内容如下图所示： 接下来，我们对以上内容进行介绍。 1.main main函数是进行网络训练的程序入口，它的执行流程是读取配置信息->设置日志输出到日志文件中->加载网络模型->加.
使用Intel NCS算力棒 安装部署记录 VirtualBox With Ubuntu16.04 Source Aliyun
夜澜偶作庄周梦 酒后聊为楚客狂
12-08 3646
安装环境：VirtualBox  操作系统：Ubuntu 16.04 Desktop 注：VirtualBox需要安装扩展包，后面需要用到USB3.0 1、安装ubuntu，安装完毕后ubutu系统中切换镜像源，如切换成阿里云等，开始升级ubuntu sudo apt-get update sudo apt-get upgrade #deb包 deb http://mirrors.aliy...
YOLO V8-Segment 【单张图片推理】推理详解及部署实现
最新发布
牧子川的博客
07-16 2356
weights：模型权重文件的路径。默认为 "yolov8n.pt"。device (torch.device)：运行模型的设备。默认为 CPU。dnn：使用 OpenCV DNN 模块进行 ONNX 推断。默认为假。data：包含类名的附加 data.yaml 文件的路径。可选。fp16：启用半精度推理。仅在特定后端支持。默认为 False。fuse：融合 Conv2D + BatchNorm 层进行优化。默认为 True。verbose：启用详细日志记录。默认为 True。
开源代码文献
cv_family_z的博客
11-18 2万+
持续跟新Tracking: Learning to Track: Online Multi-Object Tracking by Decision Making ICCV2015 使用 Markov Decision Processes 做跟踪，速度可能比较慢,效果应该还可以 https://github.com/yuxng/MDP_Tracking Car detection: Int
深度卷积神经网络演化历史及结构改进脉络-40页长文全面解读
SIGAI_优快云的博客
06-18 7425
其它机器学习、深度学习算法的全面系统讲解可以阅读《机器学习-原理、算法与应用》，清华大学出版社，雷明著，由SIGAI公众号作者倾力打造。 书的购买链接 书的勘误，优化，源代码资源 导言从1989年LeCun提出第一个真正意义上的卷积神经网络到今天为止，它已经走过了29个年头。自2012年AlexNet网络出现之后，最近6年以来，卷积神经网络得到了急速发展，在很多问题上取...
caffe中的siamese network（一）
深藏功与名
11-04 8816
caffe中里面的一个例子，我理解就是一个双输入网络。可以参考http://blog.csdn.net/hjimce/article/details/50098483 看图说话： 、 篇幅太长了，看看图就明白了。
Siamese-RPN目标跟踪算法的实现
qq_35945399的博客
08-27 2964
关于Siamese-RPN目标跟踪算法的实现，参见我的github: https://github.com/zkisthebest/Siamese-RPN
【目标跟踪】SiameseRPN：High Performance Visual Tracking with Siamese Region Proposal Network
kobe_huai的博客
06-06 1万+
今年CVPR2018 tracking的文章Siamese类方法很火，之前介绍了一篇FlowTrack 今天更新一篇CVPR2018的Spotlight 原文链接：High Performance Visual Tracking with Siamese Region Proposal Network Motivation & Summary 作者的motivation很简单，...
Fully-Convolutional Siamese Networks for Object Tracking基于全卷积孪生网络的目标跟踪算法SiameseFC
宿莽的专栏
03-12 1万+
1.论文相关Bertinetto, Luca, et al. "Fully-convolutional siamese networks for object tracking." European conference on computer vision. Springer, Cham, 2016.之前写的论文总结更多的偏向于论文翻译，以后写的论文总结要更偏向于总结2.论文内容2.1摘要   ...
【论文笔记】目标跟踪算法之Siamese-FC
LCCFlccf的博客
04-07 3165
Fully-Convolutional Siamese Networks for Object Tracking 文章目录Fully-Convolutional Siamese Networks for Object Tracking一、简介二、补充知识三、算法总体框架四、具体实现4.1 数据集的处理4.2 损失函数五、实验结果 一、简介 目标跟踪和目标检测还不一样，它不是对每一帧的detect...
深度学习（十六）基于2-channel network的图片相似度判别
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hjimce的专栏
11-29 7万+
本篇博文主要讲解2015年CVPR的一篇关于图像相似度计算的文章：《Learning to Compare Image Patches via Convolutional Neural Networks》，本篇文章相比于经典的算法Siamese Networks 精度上有比较不错的提高。学习这篇paper的算法，需要熟悉Siamese Networks（经典老文献《Signature Verification Using a Siamese Time Delay Neural Network》）、以及大神何
基于全连接孪生网络的目标跟踪（siamese-fc）
autocyz
11-18 4万+
Fully-Convolutional Siamese Networks for Object Tracking这两年可以说deeplearning已经占领了visual object tracking这个领域，但是对于跟踪问题来说，这些基于DL的做法虽然能够很好的提升跟踪的效果，但是在时效性这一方面却做的很差，这是因为DL复杂的模型往往需要很大的计算量，尤其是当使用的DL模型在跟踪的时候对模型进行
目标追踪（一）：siamese-fc
qq_28778507的博客
04-15 2006
这是2016CVPR收录的 在此之前，目标追踪一般都是online的，在这里引入了深度学习offline的训练，他提出了一种思想：similarity learning 他有三大contribution： 首先在速度上可以实现实时检测 通过一个范本来定位想要的被追踪物体（detection物体为卷积核，卷积下一帧的feature map） 使用了全卷积网络 他的 缺点： 如果物体size变化...
Siamese跟踪系列文章汇总
yxyyxy1995的博客
06-25 1311
参考链接 https://zhuanlan.zhihu.com/p/66757733 https://zhuanlan.zhihu.com/p/42546692 Siam FC SiamRPN DaSianRPN SuanRPN ++
caffe/caffe.hpp: No such file or directory
03-20
caffe/caffe.hpp是Caffe深度学习框架的头文件之一，它包含了Caffe框架的主要功能和接口。如果你在编译或者运行代码时遇到了"caffe/caffe.hpp: No such file or directory"的错误提示，可能是因为编译器无法找到该...
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