:: 4.Image Tracking 스마트카트(OpenCV based)

In this tutorial you'll learn two methods you can use to perform real-time object detection using deep learning on the Raspberry Pi with OpenCV and Python.

www.pyimagesearch.com

A few weeks ago I demonstrated how to perform real-time object detection using deep learning and OpenCV on a standard laptop/desktop.

몇주전에 난 증명했다. 어떻게 수행하는지 실시간 객체감지를 사용하여 딥러닝 & OpenCV를 on a 표준 컴퓨터상에서.

After the post was published I received a number of emails from PyImageSearch readers who were curious

if the Raspberry Pi could also be used for real-time object detection.

After the 포스트가 출판되고나서, 난 받았다. a 수많은 이메일들을 from PyImageSearch 독자들로부터 who 호기심있는

if the RPi가 could 또한 be 사용될수있을런지 실시간 객체 탐지에.

The short answer is “kind of”…

…but only if you set your expectations accordingly.

Even when applying our optimized OpenCV + Raspberry Pi install the Pi is only capable of getting up to ~0.9 frames per second when applying deep learning for object detection with Python and OpenCV.

심지어 when 적용할때 우리의 최적화된 OpenCV + RPi설치가 only 수용능력이될때 of getting

upto ~0.9 Frames를 per 초당 적용하면서 딥러닝을 for 객체탐지를위해 with Python & OpenCV와함께.

Is that fast enough?

Well, that depends on your application.

If you’re attempting to detect objects that are quickly moving through your field of view, likely
not.

충분히 빠른가 ? (의뢰자들이 흔히들 물어보는 질문이긴하지)

글쎄, 그건 네 어플에따라 달려있다.

만약 네가 시도하고있는 중이라면 to 감지하기위해 객체들을 that are 빠르게 이동중인 through 네 필드를통해 of view

But if you’re monitoring a low traffic environment with slower moving objects,

the Raspberry Pi could indeed be fast enough.

In the remainder of today’s blog post we’ll be reviewing two methods to perform deep learning-based object detection on the Raspberry Pi.

But 만약 네가 모니터링하고있는중이라면 a 적은 트레픽 환경에서 with 더느리게 이동하는 객체들과함께

the RPi는 could 과연 be 충분히 빠를수있다. (indeed::과연,참으로)

Raspberry Pi: Deep learning object detection with OpenCV

Today’s blog post is broken down into two parts.

In the first part, we’ll benchmark the Raspberry Pi for real-time object detection using OpenCV and Python.

This benchmark will come from the exact code we used

for our laptop/desktop deep learning object detector from a few weeks ago.

첫번째 파트에서, 우린 벤치마크할것이다. the RPi를 for 실시간 객체 탐지를위해 사용하여 OpenCV & 파이선을

이 벤치마크는 will 다가올것이다. from the 추출하는걸로부터 코드를 we used

for 우리의 컴퓨터 딥러닝 객체 탐지 사이트로부터 from a 몇주전에.

I’ll then demonstrate how to use multiprocessing to create an alternate method to object detection

using the Raspberry Pi.

This method may or may not be useful for your particular application,

but at the very least it will give you an idea on different methods to approach the problem.

난 그런다음 증명할것이다. how to 사용하는 멀티프로세싱을 to 만들기위해 대체방법을 to 객체 탐지에

사용하여 the

https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/

OpenCV Object Tracking - PyImageSearch

Use OpenCV to track objects in video using OpenCV's 8 object tracking algorithms, including CSRT, KCF, Boosting, MIL, TLD, MedianFlow, MOSSE, and GOTURN. Python + OpenCV object tracking code included.

www.pyimagesearch.com

https://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/

Real-time object detection with deep learning and OpenCV - PyImageSearch

In this tutorial I demonstrate how to apply object detection with deep learning and OpenCV + Python to real-time video streams and video files.

www.pyimagesearch.com

https://www.alatortsev.com/2018/09/05/installing-opencv-3-4-3-on-raspberry-pi-3-b/

Installing OpenCV 3.4.3 on Raspberry Pi 3 Model B+

Installing OpenCV 3.4.3 on Raspberry Pi 3 model B+, step-by-step.

www.alatortsev.com

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How to SSH into the Raspberry Pi - The Pi

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