vehicle-counting
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Published
20 hours ago •
kaylode
kaylode
Vehicle counting using Pytorch
🏍️ 🚙 Vehicle Tracking using YOLOv5 + DeepSORT 🚌
Dev logs
[19/12/2021] Update to new YOLOv5 version 6. Can load checkpoints from original repo now 🤞[16/07/2021] BIG REFACTOR Code is cleaned and working fine now, promise 🤞
[27/09/2021] All trained checkpoints on AIC-HCMC-2020 have been lost. Now use pretrained models on COCO for inference.
Method
- Use YOLOv5 for vehicle detection task, only considers objects in Region of Interest (ROI)
- Use DeepSORT for car tracking, not need to retrain this model, only inference
- Use Cosine Similarity to assign object's tracks to most similar directions.
- Count each type of vehicle on each direction.
📔 Notebook
- For inference, use this notebook
- To retrain detection model, follow instructions from original Yolov5
Dataset
- AIC-HCMC-2020: link
- Direction and ROI annotation format:
cam_01.json # match video name
{
"shapes": [
{
"label": "zone",
"points": [[x1,y1], [x2,y2], [x3,y3], [x4,y4], ... ] #Points of a polygon
},
{
"label": "direction01",
"points": [[x1,y1], [x2,y2]] #Points of vector
},
{
"label": "direction{id}",
"points": [[x1,y1], [x2,y2]]
},...
],
}
🥇 Pretrained weights
- Download finetuned models from on AIC-HCMC-2020 dataset:
| Model | Image Size | Weights | Precision | Recall | [email protected] | [email protected] |
|---|---|---|---|---|---|---|
| YOLOv5s | 640x640 | link | 0.87203 | 0.87356 | 0.91797 | 0.60795 |
| YOLOv5m | 1024x1024 | link | 0.89626 | 0.91098 | 0.94711 | 0.66816 |
🌟 Inference
- File structure
this repo
│ detect.py
└───configs
│ configs.yaml # Contains model's configurations
│ cam_configs.yaml # Contains DEEPSORT's configuration for each video
- Install dependencies by
pip install -r requirements.txt - To run full pipeline:
python run.py --input_path=<input video or dir> --output_path=<output dir> --weight=<trained weight>
-
Extra Parameters:
- --min_conf: minimum confident for detection
- --min_iou: minimum iou for detection
Results
- After running, a .csv file contains results has following example format:
| track_id | frame_id | box | color | label | direction | fpoint | lpoint | fframe | lframe |
|---|---|---|---|---|---|---|---|---|---|
| 2 | 3 | [607, 487, 664, 582] | (144, 238, 144) | 0 | 1 | (635.5, 534.5) | (977.0, 281.5) | 3 | 109 |
| 2 | 4 | [625, 475, 681, 566] | (144, 238, 144) | 0 | 1 | (635.5, 534.5) | (977.0, 281.5) | 3 | 109 |
| 2 | 5 | [631, 471, 686, 561] | (144, 238, 144) | 0 | 1 | (635.5, 534.5) | (977.0, 281.5) | 3 | 109 |
- With:
-
track_id: the id of the object -
frame_id: the current frame -
box: the box wraps around the object in the corresponding frame -
color: the color which is used to visualize the object -
direction: the direction of the object -
fpoint,lpoint: first/last coordinate where the object appears -
fframe,lframe: first/last frame where the object appears
-
| Visualization result |
|---|
 |
 |
References
- DeepSORT from https://github.com/ZQPei/deep_sort_pytorch
- YOLOv5 from https://github.com/ultralytics/yolov5
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