omar16100 / fall-detection

Lightweight fall detection based on human pose estimation

Lightweight fall detection based on human pose estimation

The goal is to be able to deploy it on a Raspberry 3 Model B+ with a webcam and an Intel® Neural Compute Stick 2. It should push a warning on other devices if a person has fallen. In a further step, it is planned to create a web video surveillance dashboard, and having several live streams including pose estimation from several Raspberries.

This repository is based on PINTO0309/MobileNetV2-PoseEstimation which itself is based on ildoonet/tf-pose-estimation.

An interesting and simple approach using the Y-axis movement of the head position to detect falls: https://github.com/reigngt09/Pose-Estimation/tree/master/3.%20Fall%20Detection

We hope that performance will be sufficient to work with these models, else we would have to go for more simpler models.

Run on Movidius stick:

python3 fall_detection.py -d MYRIAD -b True

Notes

• Installing numba on Raspberry: numba/numba#3670 (comment)
• On Raspberry with Movidius 2.0 FPS, NCS2 2.3 FPS

Original README follows:

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MobileNetV2-PoseEstimation

[Caution] The behavior of RraspberryPi+NCS2 is very unstable.
[Caution] The behavior of Tensorflow Lite+CPU is unstable.
[Caution] May 06, 2019, The Google Edge TPU program and model are under construction.

Introduction

This repository has its own implementation, impressed by ildoonet's achievements.
Thank you, ildoonet.
https://github.com/ildoonet/tf-pose-estimation.git

I will make his implementation even faster with CPU only.

Environment

• Ubuntu 16.04 x86_64
• OpenVINO 2019 R1.0.1
• Tensorflow v1.12.0 + Tensorflow Lite
• USB Camera
• Neural Compute Stick 2 (NCS2)
• Google Edge TPU
• Python 3.5

Environment construction and training procedure

Learn "Openpose" from scratch with MobileNetv2 + MS-COCO and deploy it to OpenVINO/TensorflowLite Part.1

Learn "Openpose" from scratch with MobileNetv2 + MS-COCO and deploy it to OpenVINO/TensorflowLite (Inference by OpenVINO/NCS2) Part.2

Core i7 only + OpenVINO + Openpose Large model + Sync mode (disabled GPU)

NCS2 x1 + OpenVINO + Openpose Large model + Async + Normal mode

Core i7 only + OpenVINO + Openpose Small model + Sync + Boost mode (disabled GPU)

NCS2 x1 + OpenVINO + Openpose Small model + Async + Boost mode

Usage

$ git clone https://github.com/PINTO0309/MobileNetV2-PoseEstimation.git
$ cd MobileNetV2-PoseEstimation

CPU - Sync Mode

$ python3 openvino-usbcamera-cpu-ncs2-sync.py -d CPU

CPU - Sync + Boost Mode

$ python3 openvino-usbcamera-cpu-ncs2-sync.py -d CPU -b True

NCS2 - Sync Mode

$ python3 openvino-usbcamera-cpu-ncs2-sync.py -d MYRIAD

CPU - Async Mode

$ python3 openvino-usbcamera-cpu-ncs2-async.py -d CPU

NCS2 - Async - Single Stick Mode

$ python3 openvino-usbcamera-cpu-ncs2-async.py -d MYRIAD

NCS2 - Async - Multi Stick Mode

$ python3 openvino-usbcamera-cpu-ncs2-async.py -d MYRIAD -numncs 2

NCS2 - Async - Single Stick + Boost Mode

$ python3 openvino-usbcamera-cpu-ncs2-async.py -d MYRIAD -b True

GPU (Intel HD series only) - Async - Boost Mode

$ python3 openvino-usbcamera-cpu-ncs2-async.py -d GPU -b True

Reference articles, Very Thanks!!

https://github.com/ildoonet/tf-pose-estimation.git
https://www.tensorflow.org/api_docs/python/tf/image/resize_area
Python OpenCVの基礎 resieで画像サイズを変えてみる - Pythonの学習の過程とか - ピーハイ
Blurring and Smoothing - OpenCV with Python for Image and Video Analysis 8
https://www.learnopencv.com/deep-learning-based-human-pose-estimation-using-opencv-cpp-python/
https://teratail.com/questions/169393