Roboflow Object Tracking Example
Object tracking using Roboflow Inference API and Zero-Shot (CLIP) Deep SORT. Read more in our Zero-Shot Object Tracking announcement post.
Example object tracking courtesy of the Roboflow Universe public Aquarium model and dataset. You can adapt this to your own dataset on Roboflow or any pre-trained model from Roboflow Universe.
Overview
Object tracking involves following individual objects of interest across frames. It combines the output of an object detection model with a secondary algorithm to determine which detections are identifying "the same" object over time.
Previously, this required training a special classification model to differentiate the instances of each different class. In this repository, we have used OpenAI's CLIP zero-shot image classifier to create a universal object tracking repository. All you need is a trained object detection model and CLIP handles the instance identification for the object tracking algorithm.
Getting Started
Colab Tutorial Here:
Training your model
To use the Roboflow Inference API as your detection engine:
Upload, annotate, and train your model on Roboflow with Roboflow Train. Your model will be hosted on an inference URL.
To use YOLOv7 as your detection engine:
Follow Roboflow's Train YOLOv7 on Custom Data Tutorial
The YOLOv7 implementation uses this colab notebook
To use YOLOv5 as your detection engine:
Follow Roboflow's Train YOLOv5 on Custom Data Tutorial
The YOLOv5 implementation uses this colab notebook
The YOLOv5 implementation is currently compatible with this commit hash of YOLOv5 886f1c03d839575afecb059accf74296fad395b6
Performing Object Tracking
Clone repositories
git clone https://github.com/roboflow-ai/zero-shot-object-tracking
cd zero-shot-object-tracking
git clone https://github.com/openai/CLIP.git CLIP-repo
cp -r ./CLIP-repo/clip ./clip // Unix based
robocopy CLIP-repo/clip clip\ // Windows
Install requirements (python 3.7+)
pip install --upgrade pip
pip install -r requirements.txtInstall requirements (anaconda python 3.8)
conda install pytorch torchvision torchaudio -c pytorch
conda install ftfy regex tqdm requests pandas seaborn
pip install opencv pycocotools tensorflow
Run with Roboflow
python clip_object_tracker.py --source data/video/fish.mp4 --url https://detect.roboflow.com/playing-cards-ow27d/1 --api_key ROBOFLOW_API_KEY --info**NOTE you must provide a valid API key from Roboflow
Run with YOLOv7
python clip_object_tracker.py --weights models/yolov7.pt --source data/video/fish.mp4 --detection-engine yolov7 --infoRun with YOLOv5
python clip_object_tracker.py --weights models/yolov5s.pt --source data/video/fish.mp4 --detection-engine yolov5 --infoRun with YOLOv4
To use YOLOv4 for object detection you will need pretrained weights (.weights file), a model config for your weights (.cfg), and a class names file (.names). Test weights can be found here https://github.com/AlexeyAB/darknet. yolov4.weights yolov4.cfg
python clip_object_tracker.py --weights yolov4.weights --cfg yolov4.cfg --names coco.names --source data/video/cars.mp4 --detection-engine yolov4 --info
(by default, output will be in runs/detect/exp[num])
Help
python clip_object_tracker.py -h--weights WEIGHTS [WEIGHTS ...] model.pt path(s)
--source SOURCE source (video/image)
--img-size IMG_SIZE inference size (pixels)
--confidence CONFIDENCE object confidence threshold
--overlap OVERLAP IOU threshold for NMS
--thickness THICKNESS Thickness of the bounding box strokes
--device DEVICE cuda device, i.e. 0 or 0,1,2,3 or cpu
--view-img display results
--save-txt save results to *.txt
--save-conf save confidences in --save-txt labels
--classes CLASSES [CLASSES ...] filter by class: --class 0, or --class 0 2 3
--agnostic-nms class-agnostic NMS
--augment augmented inference
--update update all models
--project PROJECT save results to project/name
--name NAME save results to project/name
--exist-ok existing project/name ok, do not increment
--nms_max_overlap Non-maxima suppression threshold: Maximum detection overlap.
--max_cosine_distance Gating threshold for cosine distance metric (object appearance).
--nn_budget NN_BUDGET Maximum size of the appearance descriptors allery. If None, no budget is enforced.
--api_key API_KEY Roboflow API Key.
--url URL Roboflow Model URL.
--info Print debugging info.
--detection-engine Which engine you want to use for object detection (yolov7, yolov5, yolov4, roboflow).
Acknowledgements
Huge thanks to:
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
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