深度学习与应用：行人跟踪

发布于：2024-10-13 ⋅ 阅读:(50) ⋅ 点赞:(0)
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**实验  深度学习与应用：行人跟踪 **
------
**1、 实验目的**
------
- 了解行人跟踪模型基础处理流程
- 熟悉行人跟踪模型的基本原理
- 掌握 行人跟踪模型的参数微调训练以及推理的能力
- 掌握行人跟踪模型对实际问题的应用能力，了解如何在特定的场景和任务中应用该模型


**2、实验环境**
------
**[镜像详情]**
虚拟机数量：1个（需GPU  >=4GB）
虚拟机信息：

1. 操作系统：Ubuntu20.04

2. 代码位置：/home/zkpk/experiment/yolo_tracking_main

3. MOT17数据集存储位置：examples/val_utils/data/MOT17
   (数据集下载地址：Https://motchallenge.net)

4. 已安装软件：python版本：python 3.9，显卡驱动，cuda版本：cuda11.3 cudnn 版本：8.4.1,torch==1.12.1+cu113,torchvision= 0.13.1+cu113
5. 根据requirements.txt,合理配置python环境

**3、实验内容**
------
- 准备多目标跟踪数据集MOT17 ，下载地址位于（Https://motchallenge.net），放置于工程路径为：（examples/val_utils/data/MOT17）  
- 根据不用的行人跟踪算法实现行人跟踪实验
- 根据实验效果微调行人跟踪算法模型参数
- 实现离线视频的行人跟踪


**4、实验关键点**
------
-  下载数据集放置于指定的文件夹下
-  配置好算法所需的python虚拟环境
-  掌握行人跟踪所需的算法基础
-  具备一定的代码能力，解决实际问题

   
**5、实验效果图**
------
行人跟踪效果截图：
![](media/798ashdh.png)
  <center>图 1</center>  

行人跟踪视频效果：

目标跟踪

**6、实验步骤**
------
- 6.1 准备数据集，下载多目标跟踪数据集MOT17 ，下载地址位于（Https://motchallenge.net），将数据集放置于（examples/val_utils/data/MOT17）路径，如下图所示：
  
  <center>图 1</center>  
- 6.2 实现行人跟踪方法对视频的实时检测，运行一下命令进入yolo_tracking_main\examples：  
  
  ```shell
  cd   /home/zkpk/experiment/yolo_tracking_main/examples
  ```
运行python的track.py脚本，命令如下：
```shell
python --yolo-model weights/yolov8n --tracking-method  deepocsort  ----reid-model  weights/lmbn_n_cuhk03_d.pt  --source  testvideo.mp4   --conf  0.3  --iou  0.5  
                                                         botsort  
                                                         strongsort
                                                         ocsort  
                                                         bytetrack

```
分别对应5种不同的目标跟踪模型，实现对行人目标的跟踪

运行日志如下：
```
Successfully loaded imagenet pretrained weights from "weights\osnet_x1_0_imagenet.pth"
video 1/1 (1/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 63.4ms
video 1/1 (2/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (3/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (4/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (5/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (6/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (7/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (8/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (9/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (10/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (11/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (12/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 9.0ms
video 1/1 (13/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (14/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (15/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (16/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (17/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 9.0ms
video 1/1 (18/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (19/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (20/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (21/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (22/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (23/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (24/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 15.0ms
video 1/1 (25/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (26/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (27/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (28/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (29/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 4 persons, 13.0ms
video 1/1 (30/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 4 persons, 13.0ms
video 1/1 (31/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (32/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms

```
6.3 根据上一步骤6.3 行人跟踪的效果，假如不理想可以使用MOT17数据集微调模型参数(在配置好数据集的情况才可以微调)，运行一下命令：
``` shell
python  --yolo-model weights/yolov8n.pt --tracking-method  deepocsort --benchmark  MOT17  --conf  0.45

```

微调参数过程日志如下：

```
2023-11-17 17:34:48.482 | INFO     | val:eval:204 - Staring evaluation process on E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1
2023-11-17 17:34:48.560 | INFO     | val:eval:204 - Staring evaluation process on E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1


2023-11-17 17:35:00.221 | SUCCESS  | boxmot.appearance.reid_model_factory:load_pretrained_weights:207 - Successfully loaded pretrained weights from "E:\PycharmProjects\yolo_tracking_main\examples\weights\osnet_x0_25_msmt17.pt"
2023-11-17 17:35:00.221 | WARNING  | boxmot.appearance.reid_model_factory:load_pretrained_weights:211 - The following layers are discarded due to unmatched keys or layer size: ('classifier.weight', 'classifier.bias')
2023-11-17 17:35:00.228 | SUCCESS  | boxmot.appearance.reid_model_factory:load_pretrained_weights:207 - Successfully loaded pretrained weights from "E:\PycharmProjects\yolo_tracking_main\examples\weights\osnet_x0_25_msmt17.pt"
2023-11-17 17:35:00.228 | WARNING  | boxmot.appearance.reid_model_factory:load_pretrained_weights:211 - The following layers are discarded due to unmatched keys or layer size: ('classifier.weight', 'classifier.bias')
image 1/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000001.jpg: 736x1280 11 persons, 610.4ms
image 1/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000001.jpg: 736x1280 25 persons, 652.3ms
image 2/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000002.jpg: 736x1280 9 persons, 442.2ms
image 2/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000002.jpg: 736x1280 22 persons, 454.5ms
image 3/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000003.jpg: 736x1280 9 persons, 370.0ms
image 3/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000003.jpg: 736x1280 24 persons, 450.9ms
image 4/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000004.jpg: 736x1280 9 persons, 460.8ms
image 4/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000004.jpg: 736x1280 23 persons, 385.0ms
image 5/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000005.jpg: 736x1280 10 persons, 460.4ms
image 5/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000005.jpg: 736x1280 22 persons, 399.6ms
image 6/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000006.jpg: 736x1280 10 persons, 443.0ms
image 7/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000007.jpg: 736x1280 10 persons, 460.8ms
image 6/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000006.jpg: 736x1280 22 persons, 429.7ms
image 8/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000008.jpg: 736x1280 10 persons, 434.5ms
image 7/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000007.jpg: 736x1280 24 persons, 448.3ms
image 9/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000009.jpg: 736x1280 10 persons, 386.9ms
image 8/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000008.jpg: 736x1280 23 persons, 476.7ms
image 10/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000010.jpg: 736x1280 11 persons, 869.1ms
image 9/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000009.jpg: 736x1280 23 persons, 453.9ms
image 11/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000011.jpg: 736x1280 11 persons, 460.8ms
image 10/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000010.jpg: 736x1280 23 persons, 428.2ms
image 12/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000012.jpg: 736x1280 9 persons, 439.9ms
image 11/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000011.jpg: 736x1280 19 persons, 470.3ms
image 13/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000013.jpg: 736x1280 10 persons, 440.8ms
image 12/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000012.jpg: 736x1280 19 persons, 460.8ms
image 14/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000014.jpg: 736x1280 9 persons, 434.2ms
image 13/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000013.jpg: 736x1280 20 persons, 439.0ms
image 15/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000015.jpg: 736x1280 8 persons, 384.9ms
image 14/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000014.jpg: 736x1280 20 persons, 440.8ms
image 16/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000016.jpg: 736x1280 8 persons, 462.8ms
image 15/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000015.jpg: 736x1280 20 persons, 451.8ms
image 17/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000017.jpg: 736x1280 8 persons, 470.7ms
image 16/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000016.jpg: 736x1280 22 persons, 486.0ms
image 18/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000018.jpg: 736x1280 7 persons, 410.9ms
image 17/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000017.jpg: 736x1280 23 persons, 425.9ms
image 19/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000019.jpg: 736x1280 7 persons, 380.0ms
image 20/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000020.jpg: 736x1280 8 persons, 436.8ms
image 18/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000018.jpg: 736x1280 23 persons, 447.8ms
image 21/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000021.jpg: 736x1280 8 persons, 476.0ms
image 19/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000019.jpg: 736x1280 21 persons, 518.6ms
image 22/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000022.jpg: 736x1280 8 persons, 360.0ms
image 20/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000020.jpg: 736x1280 22 persons, 388.6ms
image 23/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000023.jpg: 736x1280 9 persons, 391.0ms
image 21/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000021.jpg: 736x1280 22 persons, 416.9ms
image 24/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000024.jpg: 736x1280 9 persons, 458.8ms
image 22/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000022.jpg: 736x1280 22 persons, 404.7ms
image 25/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000025.jpg: 736x1280 9 persons, 443.9ms
```

**7、思考题**
------
-  考虑在行人跟踪中，模型算法还有哪些改进点
-  思考怎么将跟踪算法模型应用到手部动作跟踪中
-  思考如何调节模型参数和训练参数提升模型的效果指标

**8、 实验报告**
------
请按照实验报告的格式要求撰写实验报告。

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