Title: Sneak Preview: Raspberry Pi for Computer Vision - PyImageSearch

URL Source: https://pyimagesearch.com/2019/04/04/sneak-preview-raspberry-pi-for-computer-vision/

Published Time: 2019-04-04T14:00:53+00:00

Markdown Content:
**The Kickstarter launch date of Wednesday, April 10th is approaching so fast!**

I still have a ton of work left to do and I’m currently neck-deep in Kickstarter campaign logistics, [**but I took a few minutes and recorded this sneak preview of _Raspberry Pi for Computer Vision_ just for you:**](https://pyimagesearch.wistia.com/medias/mdjxjmofjv)

![Image 1](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%200%200'%3E%3C/svg%3E)

The video is fairly short, clocking in at 5m56s, and it’s _absolutely_ worth the watch, but if you don’t have enough time to watch it, you can read the gist below:

*   0m06s: I show an example of **building a CV + IoT wildlife camera.** We’ll be covering the _exact_ implementation inside the book.
*   0m29s: I discuss the Raspberry Pi, it’s compatibility with cameras + computer vision libraries, and **how we can use the Pi for computer vision.**
*   0m45: Can we write software that understand and take action based on what the Pi “sees”? **Absolutely, yes!** And I’ll show you how through face recognition, footfall/traffic counter applications, gesture recognition, deep learning on the Pi, _and much more!_
*   1m01s: Regardless of whether you’re an experienced computer vision and deep learning practitioner or you’re brand new to the world of computer vision and image processing, this book will help you build practical, real-world computer vision applications on the Raspberry Pi.
*   1m16s: I provide a **high level overview of the topics that will be covered** inside the RPi + CV book.
*   1m45s: Since this book covers such a large, diverse amount of content, I’ve decided to break the book down into three _volumes_, called **_“bundles”._** You’ll be able to choose a bundle based on how in-depth you want to study CV and DL on the Pi, which projects/chapters interest you the most, along with your particular budget.
*   2m50s: I’m a strong believer of **learning by doing.** You’ll roll up your sleeves, get your hands dirty in code, and build actual, real-world projects.
*   3m03s: Demos of IoT wildlife monitoring, detecting tired drivers behind the wheel, pan and tilt object tracking, and IoT traffic/footfall counting.
*   3m41s: More demos, including hand gesture recognition, vehicle detection and recognition, vehicle speed detection, multiple Pis and deep learning, self-driving car applications, Movidius NCS and OpenVINO, face recognition security camera, smart classroom attendance system.
*   **The Kickstarter campaign will be going live on _Wednesday, April 10th at 10AM EDT_**— I hope to see you on the Kickstarter backer list!

Like I said, if you have the time, [the sneak preview video is definitely worth the watch.](https://pyimagesearch.wistia.com/medias/mdjxjmofjv)

I hope that you decide to support the _Raspberry Pi for Computer Vision_ Kickstarter campaign on **Wednesday, April 10th at 10AM EDT** — if you’re ready to bring CV and DL to embedded/IoT devices, then this is the _perfect_ book for you!

To be notified when more Kickstarter announcements go live, [**be sure to signup for the RPi for CV Kickstarter notification list!**](https://app.monstercampaigns.com/c/lmpypnxz7n0g1h4v2puy/)

![Image 2](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20410%20567'%3E%3C/svg%3E)

#### Join the PyImageSearch Newsletter and Grab My FREE 17-page Resource Guide PDF

Enter your email address below to **join the PyImageSearch Newsletter** and **download my FREE 17-page Resource Guide PDF** on Computer Vision, OpenCV, and Deep Learning.

![Image 3](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20240%20240'%3E%3C/svg%3E)

#### **About the Author**

Hi there, I’m Adrian Rosebrock, PhD. All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV. I created this website to show you what I believe is the best possible way to get your start.