🏍️ 🚙 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	MAP@0.5	MAP@0.5-0.95
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	direction	fpoint	lpoint	fframe	lframe
2	3	[607, 487, 664, 582]	(144, 238, 144)	1	(635.5, 534.5)	(977.0, 281.5)	3	109
2	4	[625, 475, 681, 566]	(144, 238, 144)	1	(635.5, 534.5)	(977.0, 281.5)	3	109
2	5	[631, 471, 686, 561]	(144, 238, 144)	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

License

kaylode/vehicle-counting

ErrorLooks like something went wrong!

About

Topics

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Error
Looks like something went wrong!

Packages