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Detect, track, and count cars in a video
Use auto-labeling to create a model from a video, then use the model to annotate a video
Artificial Intelligence
Whether you are counting cars on a road or products on a conveyer belt, there are many use cases for computer vision with video. With video as input, auto-labeling can be used to create a better classifier with less manual effort. This Code Pattern shows you how to create and use a classifier to identify objects in motion and then track the objects and count them as they enter designated regions of interest.
By Alex and ...
N/A
- TBD
In this Code Pattern, we will create a video car counter using PowerAI Vision Video Data Platform, OpenCV and a Jupyter Notebook. We'll use a little manual labeling and a lot of auto-labeling to train an object classifier to recognize cars on a highway. We'll load another car video into a Jupyter Notebook where we'll process the individual frames and annotate the video.
We'll use our deployed model for inference to detect cars on a sample of the frames at a regular interval. We'll use OpenCV to track the cars from frame to frame in between inference. In addition to counting the cars as they are detected, we'll also count them as they cross a "finish line" for each lane and show cars per second.
When the reader has completed this Code Pattern, they will understand how to:
- Goal 1
- Goal 2
- Goal 3
- User interaction 1.
- User interaction 2.
- User interaction 3.
- Jupyter Notebook: An open source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.
- OpenCV: Open source computer vision library.
- Nimbix Cloud Computing Platform: An HPC & Cloud Supercomputing platform enabling engineers, scientists & developers, to build, compute, analyze, and scale simulations in the cloud.
- Artificial Intelligence: Artificial intelligence can be applied to disparate solution spaces to deliver disruptive technologies.
- Python: Python is a programming language that lets you work more quickly and integrate your systems more effectively.
- Computer vision: Read about computer vision on Wikipedia.