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1 | -# YOLOv4 + Deep_SORT | ||
2 | - | ||
3 | -<img src="https://github.com/yehengchen/Object-Detection-and-Tracking/blob/master/OneStage/yolo/deep_sort_yolov4/output/comparison.png" width="81%" height="81%"> <img src="https://github.com/yehengchen/video_demo/blob/master/video_demo/output.gif" width="40%" height="40%"> <img src="https://github.com/yehengchen/video_demo/blob/master/video_demo/TownCentreXVID_output.gif" width="40%" height="40%"> | ||
4 | - | ||
5 | -__Object Tracking & Counting Demo - [[BiliBili]](https://www.bilibili.com/video/BV1Ug4y1i71w#reply3014975828) [[Chinese Version]](https://blog.csdn.net/weixin_38107271/article/details/96741706)__ | ||
6 | -## Requirement | ||
7 | -__Development Environment: [Deep-Learning-Environment-Setup](https://github.com/yehengchen/Ubuntu-16.04-Deep-Learning-Environment-Setup)__ | ||
8 | - | ||
9 | -* OpenCV | ||
10 | -* sklean | ||
11 | -* pillow | ||
12 | -* numpy 1.15.0 | ||
13 | -* torch 1.3.0 | ||
14 | -* tensorflow-gpu 1.13.1 | ||
15 | -* CUDA 10.0 | ||
16 | -*** | ||
17 | - | ||
18 | -It uses: | ||
19 | - | ||
20 | -* __Detection__: [YOLOv4](https://github.com/yehengchen/Object-Detection-and-Tracking/tree/master/OneStage/yolo/Train-a-YOLOv4-model) to detect objects on each of the video frames. - 用自己的数据训练YOLOv4模型 | ||
21 | - | ||
22 | -* __Tracking__: [Deep_SORT](https://github.com/nwojke/deep_sort) to track those objects over different frames. | ||
23 | - | ||
24 | -*This repository contains code for Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT). We extend the original SORT algorithm to integrate appearance information based on a deep appearance descriptor. See the [arXiv preprint](https://arxiv.org/abs/1703.07402) for more information.* | ||
25 | - | ||
26 | -## Quick Start | ||
27 | - | ||
28 | -__0.Requirements__ | ||
29 | - | ||
30 | - pip install -r requirements.txt | ||
31 | - | ||
32 | -__1. Download the code to your computer.__ | ||
33 | - | ||
34 | - git clone https://github.com/yehengchen/Object-Detection-and-Tracking.git | ||
35 | - | ||
36 | -__2. Download [[yolov4.weights]](https://drive.google.com/file/d/1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT/view) [[Baidu]](https://pan.baidu.com/s/1jRudrrXAS3DRGqT6mL4L3A ) - `mnv6`__ and place it in `deep_sort_yolov4/model_data/` | ||
37 | - | ||
38 | -*Here you can download my trained [[yolo4_weight.h5]](https://pan.baidu.com/s/1JuT4KCUFaE2Gvme0_S37DQ ) - `w17w` weights for detecting person/car/bicycle,etc.* | ||
39 | - | ||
40 | -__3. Convert the Darknet YOLO model to a Keras model:__ | ||
41 | -``` | ||
42 | -$ python convert.py model_data/yolov4.cfg model_data/yolov4.weights model_data/yolo.h5 | ||
43 | -``` | ||
44 | -__4. Run the YOLO_DEEP_SORT:__ | ||
45 | - | ||
46 | -``` | ||
47 | -$ python main.py -c [CLASS NAME] -i [INPUT VIDEO PATH] | ||
48 | - | ||
49 | -$ python main.py -c person -i ./test_video/testvideo.avi | ||
50 | -``` | ||
51 | - | ||
52 | -__5. Can change [deep_sort_yolov3/yolo.py] `__Line 100__` to your tracking object__ | ||
53 | - | ||
54 | -*DeepSORT pre-trained weights using people-ReID datasets only for person* | ||
55 | -``` | ||
56 | - if predicted_class != args["class"]: | ||
57 | - continue | ||
58 | - | ||
59 | - if predicted_class != 'person' and predicted_class != 'car': | ||
60 | - continue | ||
61 | -``` | ||
62 | - | ||
63 | -## Train on Market1501 & MARS | ||
64 | -*People Re-identification model* | ||
65 | - | ||
66 | -[cosine_metric_learning](https://github.com/nwojke/cosine_metric_learning) for training a metric feature representation to be used with the deep_sort tracker. | ||
67 | - | ||
68 | -## Citation | ||
69 | - | ||
70 | -### YOLOv4 : | ||
71 | - | ||
72 | - @misc{bochkovskiy2020yolov4, | ||
73 | - title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, | ||
74 | - author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, | ||
75 | - year={2020}, | ||
76 | - eprint={2004.10934}, | ||
77 | - archivePrefix={arXiv}, | ||
78 | - primaryClass={cs.CV} | ||
79 | - } | ||
80 | - | ||
81 | -### Deep_SORT : | ||
82 | - | ||
83 | - @inproceedings{Wojke2017simple, | ||
84 | - title={Simple Online and Realtime Tracking with a Deep Association Metric}, | ||
85 | - author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich}, | ||
86 | - booktitle={2017 IEEE International Conference on Image Processing (ICIP)}, | ||
87 | - year={2017}, | ||
88 | - pages={3645--3649}, | ||
89 | - organization={IEEE}, | ||
90 | - doi={10.1109/ICIP.2017.8296962} | ||
91 | - } | ||
92 | - | ||
93 | - @inproceedings{Wojke2018deep, | ||
94 | - title={Deep Cosine Metric Learning for Person Re-identification}, | ||
95 | - author={Wojke, Nicolai and Bewley, Alex}, | ||
96 | - booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)}, | ||
97 | - year={2018}, | ||
98 | - pages={748--756}, | ||
99 | - organization={IEEE}, | ||
100 | - doi={10.1109/WACV.2018.00087} | ||
101 | - } | ||
102 | - | ||
103 | -## Reference | ||
104 | -#### Github:deep_sort@[Nicolai Wojke nwojke](https://github.com/nwojke/deep_sort) | ||
105 | -#### Github:deep_sort_yolov3@[Qidian213 ](https://github.com/Qidian213/deep_sort_yolov3) | ||
106 | -#### Github:Deep-SORT-YOLOv4@[LeonLok](https://github.com/LeonLok/Deep-SORT-YOLOv4) | ||
107 | - |
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