https://github.com/adipandas/multi-object-tracker
Multi-object trackers in Python
https://github.com/adipandas/multi-object-tracker
caffe computer-vision deep-learning iou-tracker multi-object-tracking neural-network object-detection object-tracking opencv python python3 ssd-mobilenet tensorflow tracker yolo yolov3
Last synced: about 2 months ago
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Multi-object trackers in Python
- Host: GitHub
- URL: https://github.com/adipandas/multi-object-tracker
- Owner: adipandas
- License: mit
- Created: 2018-09-11T15:25:03.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-10-10T18:57:39.000Z (over 1 year ago)
- Last Synced: 2025-02-15T15:19:21.147Z (3 months ago)
- Topics: caffe, computer-vision, deep-learning, iou-tracker, multi-object-tracking, neural-network, object-detection, object-tracking, opencv, python, python3, ssd-mobilenet, tensorflow, tracker, yolo, yolov3
- Language: Python
- Homepage: https://adipandas.github.io/multi-object-tracker/
- Size: 85.7 MB
- Stars: 681
- Watchers: 21
- Forks: 102
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[cars-yolo-output]: examples/assets/cars.gif "Sample Output with YOLO"
[cows-tf-ssd-output]: examples/assets/cows.gif "Sample Output with SSD"
# Multi-object trackers in Python
Easy to use implementation of various multi-object tracking algorithms.
[](https://zenodo.org/badge/latestdoi/148338463)
`YOLOv3 + CentroidTracker` | `TF-MobileNetSSD + CentroidTracker`
:-------------------------:|:-------------------------:
![Cars with YOLO][cars-yolo-output] | ![Cows with tf-SSD][cows-tf-ssd-output]
Video source: [link](https://flic.kr/p/L6qyxj) | Video source: [link](https://flic.kr/p/26WeEWy)
## Available Multi Object Trackers
- CentroidTracker
- IOUTracker
- CentroidKF_Tracker
- SORT
## Available OpenCV-based object detectors:
- detector.TF_SSDMobileNetV2
- detector.Caffe_SSDMobileNet
- detector.YOLOv3
## Installation
Pip install for OpenCV (version 3.4.3 or later) is available [here](https://pypi.org/project/opencv-python/) and can be done with the following command:
```
pip install motrackers
```
Additionally, you can install the package through GitHub instead:
```
git clone https://github.com/adipandas/multi-object-tracker
cd multi-object-tracker
pip install [-e] .
```
**Note - for using neural network models with GPU**
For using the opencv `dnn`-based object detection modules provided in this repository with GPU, you may have to compile a CUDA enabled version of OpenCV from source.
* To build opencv from source, refer the following links:
[[link-1](https://docs.opencv.org/master/df/d65/tutorial_table_of_content_introduction.html)],
[[link-2](https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/)]
## How to use?: Examples
The interface for each tracker is simple and similar. Please refer the example template below.
```
from motrackers import CentroidTracker # or IOUTracker, CentroidKF_Tracker, SORT
input_data = ...
detector = ...
tracker = CentroidTracker(...) # or IOUTracker(...), CentroidKF_Tracker(...), SORT(...)
while True:
done, image =
if done:
break
detection_bboxes, detection_confidences, detection_class_ids = detector.detect(image)
# NOTE:
# * `detection_bboxes` are numpy.ndarray of shape (n, 4) with each row containing (bb_left, bb_top, bb_width, bb_height)
# * `detection_confidences` are numpy.ndarray of shape (n,);
# * `detection_class_ids` are numpy.ndarray of shape (n,).
output_tracks = tracker.update(detection_bboxes, detection_confidences, detection_class_ids)
# `output_tracks` is a list with each element containing tuple of
# (, , , , , , , , , )
for track in output_tracks:
frame, id, bb_left, bb_top, bb_width, bb_height, confidence, x, y, z = track
assert len(track) == 10
print(track)
```
Please refer [examples](https://github.com/adipandas/multi-object-tracker/tree/master/examples) folder of this repository for more details. You can clone and run the examples.
## Pretrained object detection models
You will have to download the pretrained weights for the neural-network models.
The shell scripts for downloading these are provided [here](https://github.com/adipandas/multi-object-tracker/tree/master/examples/pretrained_models) below respective folders.
Please refer [DOWNLOAD_WEIGHTS.md](https://github.com/adipandas/multi-object-tracker/blob/master/DOWNLOAD_WEIGHTS.md) for more details.
### Notes
* There are some variations in implementations as compared to what appeared in papers of `SORT` and `IoU Tracker`.
* In case you find any bugs in the algorithm, I will be happy to accept your pull request or you can create an issue to point it out.
## References, Credits and Contributions
Please see [REFERENCES.md](https://github.com/adipandas/multi-object-tracker/blob/master/docs/readme/REFERENCES.md) and [CONTRIBUTING.md](https://github.com/adipandas/multi-object-tracker/blob/master/docs/readme/CONTRIBUTING.md).
## Citation
If you use this repository in your work, please consider citing it with:
```
@misc{multiobjtracker_amd2018,
author = {Deshpande, Aditya M.},
title = {Multi-object trackers in Python},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/adipandas/multi-object-tracker}},
}
```