YOLOv3 + Deep_SORT 实现多类多目标检测(计数)
- OpenCV
- keras
- NumPy
- sklean
- Pillow
- tensorflow-gpu 1.10.0
It uses:
-
Detection: YOLOv3 to detect objects on each of the video frames. - 用自己的数据训练YOLOv3模型
-
Tracking: Deep_SORT to track those objects over different frames.
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 for more information.
0.Requirements
pip install -r requirements.txt
1. Download the code to your computer.
git clone https://github.com/xiaoxiong74/Object-Detection-and-Tracking.git
2. Download [yolov3.weights] and place it in deep_sort_yolov3/model_data/
Here you can download my trained [yolo-spp.h5] - t13k weights for detecting person/car/bicycle,etc.
3. Convert the Darknet YOLO model to a Keras model:
$ python convert.py model_data/yolov3.cfg model_data/yolov3.weights model_data/yolo.h5
4. Run the YOLO_DEEP_SORT:
$ python main.py -c [CLASS NAME] -i [INPUT VIDEO PATH]
$ python main.py -c person -i ./test_video/testvideo.avi
5. Can change [yolo.py] __Line 129__ to your tracking object
if predicted_class != 'person' and predicted_class != 'bicycle':
print(predicted_class)
continue
and change [main.py] __Line 108__ and __Line 123__ to your tracking object__
# __Line 108__`分别保存每个类别的track_id
if class_name == ['person']:
counter1.append(int(track.track_id))
if class_name == ['bicycle']:
counter2.append(int(track.track_id))
# __Line 123__当前画面中的每个类别单独计数
if class_name == ['person']:
i1 = i1 +1
else:
i2 = i2 +1
and change some desciption in [main.py] __Line 146__ and __Line 175__
People Re-identification model
cosine_metric_learning for training a metric feature representation to be used with the deep_sort tracker.
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
@inproceedings{Wojke2017simple,
title={Simple Online and Realtime Tracking with a Deep Association Metric},
author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
year={2017},
pages={3645--3649},
organization={IEEE},
doi={10.1109/ICIP.2017.8296962}
}
@inproceedings{Wojke2018deep,
title={Deep Cosine Metric Learning for Person Re-identification},
author={Wojke, Nicolai and Bewley, Alex},
booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018},
pages={748--756},
organization={IEEE},
doi={10.1109/WACV.2018.00087}
}