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.txt

Install 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 --info

Run with YOLOv5

python clip_object_tracker.py --weights models/yolov5s.pt --source data/video/fish.mp4 --detection-engine yolov5 --info

Run 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:

• yolov4-deepsort by theAIGuysCode
• yolov5 by ultralytics
• yolov7 by WongKinYiu
• Deep SORT Repository by nwojke
• OpenAI for being awesome