diff --git a/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/.gitattributes b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..dfe0770424b2a19faf507a501ebfc23be8f54e7b
--- /dev/null
+++ b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/.gitattributes
@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/MOT_to_UA_Detrac.m b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/MOT_to_UA_Detrac.m
new file mode 100644
index 0000000000000000000000000000000000000000..6535b96150f0519f2cad865cddf4273828b5cc1b
--- /dev/null
+++ b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/MOT_to_UA_Detrac.m
@@ -0,0 +1,53 @@
+function MOT_to_UA_Detrac(gt_file, seq_name, save_folder, img_size)
+% gt_file: MOT gt txt
+% seq_name: save name
+% save_folder: save dir
+fileID = fopen(gt_file,'r');
+A = textscan(fileID,'%d %d %d %d %d %d %d %d %d %d','Delimiter',',');
+fclose(fileID);
+
+M = [A{1},A{2},A{3},A{4},A{5},A{6},A{7},A{8},A{9}];
+max_fr = max(M(:,1));
+uniq_ids = unique(M(:,2));
+for n = 1:length(uniq_ids)
+ A{2}(M(:,2)==uniq_ids(n)) = n;
+end
+M(:,2) = A{2};
+max_id = length(uniq_ids);
+X = zeros(max_fr,max_id);
+Y = zeros(max_fr,max_id);
+W = zeros(max_fr,max_id);
+H = zeros(max_fr,max_id);
+X(M(:,1)+(M(:,2)-1)*max_fr) = M(:,3);
+Y(M(:,1)+(M(:,2)-1)*max_fr) = M(:,4);
+W(M(:,1)+(M(:,2)-1)*max_fr) = M(:,5);
+H(M(:,1)+(M(:,2)-1)*max_fr) = M(:,6);
+gtInfo.X = X;
+gtInfo.Y = Y;
+gtInfo.W = W;
+gtInfo.H = H;
+
+% visibility
+V = zeros(size(W));
+for n = 1:size(W,1)
+ idx = find(W(n,:)~=0);
+ if length(idx)<=1
+ V(n,idx) = 1;
+ continue
+ end
+ bbox = zeros(length(idx),4);
+ bbox(:,1) = X(n,idx)';
+ bbox(:,2) = Y(n,idx)';
+ bbox(:,3) = W(n,idx)';
+ bbox(:,4) = H(n,idx)';
+ [overlapRatio,~] = overlap(bbox,bbox);
+ for k = 1:length(idx)
+ overlapRatio(k,k) = 0;
+ end
+ max_overlap = max(overlapRatio,[],1);
+ V(n,idx) = 1-max_overlap;
+end
+gtInfo.V = V;
+gtInfo.img_size = img_size;
+save_path = [save_folder,'\',seq_name,'.mat'];
+save(save_path,'gtInfo');
diff --git a/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/create_pair.m b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/create_pair.m
new file mode 100644
index 0000000000000000000000000000000000000000..fec1b635417bb290a5805c273607dee6b3a172cb
--- /dev/null
+++ b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/create_pair.m
@@ -0,0 +1,63 @@
+function create_pair(dataset_dir, save_dir, num_pair, n_fold)
+% dataset_dir: cropped image dir
+% save_dir: output dir
+% num_pair: number of pairs for each fold
+% n_fold: the number of folds
+class_list = dir(dataset_dir);
+class_list(1:2) = [];
+classNum = length(class_list);
+instanceNum = zeros(1,classNum);
+for n = 1:classNum
+ temp_dir = [dataset_dir,'\',class_list(n).name,'\*.png'];
+ sub_list = dir(temp_dir);
+ instanceNum(n) = length(sub_list);
+end
+
+
+fileID = fopen(save_dir,'w');
+
+
+K = n_fold; % 10
+pairNum = num_pair; % 300
+for k = 1:K
+ for n = 1:pairNum
+ d = 0;
+ while d<1
+ temp_num = 0;
+ while temp_num<2
+ rand_class = randi(classNum);
+ class_name = class_list(rand_class).name;
+ img_list = dir([dataset_dir,'\',class_name,'\*.png']);
+ temp_num = instanceNum(rand_class);
+ end
+ choose_idx = randperm(instanceNum(rand_class),2);
+ temp_name1 = img_list(choose_idx(1)).name;
+ temp_name2 = img_list(choose_idx(2)).name;
+ dot_loc1 = find(temp_name1=='.');
+ dot_loc2 = find(temp_name2=='.');
+ idx1 = str2double(temp_name1(dot_loc1-4:dot_loc1-1));
+ idx2 = str2double(temp_name2(dot_loc2-4:dot_loc2-1));
+ d = abs(idx1-idx2);
+ end
+ fprintf(fileID,'%s %d %d\n',class_name,idx1,idx2);
+ end
+
+ for n = 1:pairNum
+ rand_class = randperm(classNum,2);
+ class_name1 = class_list(rand_class(1)).name;
+ class_name2 = class_list(rand_class(2)).name;
+ choose_idx1 = randperm(instanceNum(rand_class(1)),1);
+ choose_idx2 = randperm(instanceNum(rand_class(2)),1);
+ img_list1 = dir([dataset_dir,'\',class_name1,'\*.png']);
+ img_list2 = dir([dataset_dir,'\',class_name2,'\*.png']);
+ temp_name1 = img_list1(choose_idx1).name;
+ temp_name2 = img_list2(choose_idx2).name;
+ dot_loc1 = find(temp_name1=='.');
+ dot_loc2 = find(temp_name2=='.');
+ idx1 = str2double(temp_name1(dot_loc1-4:dot_loc1-1));
+ idx2 = str2double(temp_name2(dot_loc2-4:dot_loc2-1));
+ fprintf(fileID,'%s %d %s %d\n',class_name1,idx1,class_name2,idx2);
+ end
+end
+
+fclose(fileID);
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/crop_UA_Detrac.m b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/crop_UA_Detrac.m
new file mode 100644
index 0000000000000000000000000000000000000000..f723db526c6e931c722486ed26f165e28f12fe7a
--- /dev/null
+++ b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/General/crop_UA_Detrac.m
@@ -0,0 +1,51 @@
+function crop_UA_Detrac(gt_path, seq_name, img_folder, img_format, save_folder)
+
+% gt_path: gtInfo.mat, X,Y,W,H
+% seq_name: name of the sequence
+% img_folder: input image dir
+% img_format: input image format, etc, png, jpg
+% save_folder: cropped image dir
+
+load(gt_path)
+margin_scale = 0.15;
+resize_size = 182;
+X = gtInfo.X;
+Y = gtInfo.Y;
+W = gtInfo.W;
+H = gtInfo.H;
+img_list = dir([img_folder,'\*.',img_format]);
+for m = 1:length(img_list)
+ img_name = img_list(m).name;
+ img_path = [img_folder,'\',img_name];
+ img = imread(img_path);
+ img_size = size(img);
+ num_id = size(H,2);
+ if m>size(gtInfo.H,1)
+ continue
+ end
+ for k = 1:num_id
+ if gtInfo.H(m,k)<1
+ continue
+ end
+ xmin = round(X(m,k));
+ ymin = round(Y(m,k));
+ xmax = round(X(m,k)+W(m,k)-1);
+ ymax = round(Y(m,k)+H(m,k)-1);
+ min_side = min(xmax-xmin,ymax-ymin);
+ margin = min_side*margin_scale;
+ xmin = round(max(xmin-margin,1));
+ ymin = round(max(ymin-margin,1));
+ xmax = round(min(xmax+margin,img_size(2)));
+ ymax = round(min(ymax+margin,img_size(1)));
+ crop_img = img(ymin:ymax,xmin:xmax,:);
+ crop_img = imresize(crop_img, [resize_size,resize_size]);
+ class_name = [seq_name,'_',fileName(k,4)];
+ class_folder = [save_folder,'\',class_name];
+ if exist(class_folder,'dir')<=0
+ mkdir(class_folder)
+ end
+ id_name = [class_name,'_',fileName(m,4)];
+ save_path = [class_folder,'\',id_name,'.png'];
+ imwrite(crop_img, save_path);
+ end
+end
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/LICENSE b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..9e419e042146a2ce2e354202d4f7d8e4a3d66f31
--- /dev/null
+++ b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/LICENSE
@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
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+software and other kinds of works.
+
+ The licenses for most software and other practical works are designed
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+share and change all versions of a program--to make sure it remains free
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+any other work released this way by its authors. You can apply it to
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\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/README.md b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a4151528b5fb844a5983143cf4cce2f3d7a6d4f2
--- /dev/null
+++ b/TensorFlow/contrib/cv/TNT_ID1233_for_TensorFlow/README.md
@@ -0,0 +1,187 @@
+
+
+# 基本信息
+
+**发布者(Publisher):Huawei**
+
+**版本(Version):1.1**
+
+**修改时间(Modified) :2022.3.29**
+
+**框架(Framework):TensorFlow 1.15.0**
+
+**模型格式(Model Format):ckpt**
+
+**精度(Precision):Mixed**
+
+**处理器(Processor):昇腾910**
+
+**应用级别(Categories):Research**
+
+**描述(Description):基于TensorFlow框架的TNT网络训练代码**
+
+# 概述
+
+TNT(TrackletNet Tracker)是一种性能优秀的跟踪器。
+
+关键技术:
+
+- Tracklet-based Graph Model: 将tracklet作为顶点、将两个tracklets间相似度(的减函数)作为边权的无向图,可以通过顶点聚类算法完成 “tracklet-to-trajectory” 过程;
+- Multi-scale TrackletNet:输入两 tracklets,输出其相似度,最大特点是用时域1D滤波器充分利用了 tracklets 的时态信息(temporal info);
+- EG-IOU: 在做帧间detections关联时,使用 Epipolar Geometry(对极几何)对下一帧检测框做最佳预测,从而优化 IOU 算法;
+
+关键能力:
+
+- Graph Model 的设计可以充分使用时域信息、降低计算复杂度等;
+- TrackletNet 作为一个统一(unified)的系统,将外观信息(appearance)和时态信息(temporal)合理地结合了起来;注意,传统的时态信息一般是 bbox 的位置、大小、运动等信息,而 TrackletNet 通过时域卷积池化等,挖掘了外观信息中蕴含的时态信息(即外观信息的时域连续性)
+- EG 技术可以有效对抗相机运动带来的错误关联问题
+
+参考论文:
+
+[https://arxiv.org/abs/1811.07258](https://gitee.com/link?target=https%3A%2F%2Farxiv.org%2Fabs%2F1811.07258)
+
+参考实现:
+
+ [https://github.com/GaoangW/TNT](https://gitee.com/link?target=https%3A%2F%2Fgithub.com%2FGaoangW%2FTNT)
+
+第三方博客地址
+
+[https://blog.csdn.net/qq_42191914/article/details/103619045](https://gitee.com/link?target=https%3A%2F%2Fblog.csdn.net%2Fqq_42191914%2Farticle%2Fdetails%2F103619045)
+
+适配昇腾 AI 处理器的实现:
+
+[https://gitee.com/ascend/modelzoo/tree/master/contrib/TensorFlow/Research/cv/TNT_ID1233_for_TensorFlow
+
+通过Git获取对应commit\_id的代码方法如下:
+
+```
+git clone {repository_url} # 克隆仓库的代码
+cd {repository_name} # 切换到模型的代码仓目录
+git checkout {branch} # 切换到对应分支
+git reset --hard {commit_id} # 代码设置到对应的commit_id
+cd {code_path} # 切换到模型代码所在路径,若仓库下只有该模型,则无需切换
+```
+
+# 训练环境准备
+
+1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。
+
+2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。
+
+ 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。
+
+ **表 1** 镜像列表
+
+
+
+