Real Time Human Pose Estimation on the edge with Movidius NCS and OpenVINO

An approach towards low cost computing on the edge for vision based AI applications

Introduction

Pose estimation is a computer vision approach to detect various important parts of a human body in an image or video. It gives pixel locations of where eyes, elbows, arms, legs, etc are for one or more human bodies in an image. The algorithm gives locations of “joints” of a body. However pose is a broader subject where-from we are only focusing on human body pose estimation. None of the algorithms are perfect and are heavily dependent on the training data.

How is it useful?

Human pose detection on the edge can be used to read body language and body movement in real-time at the same location as the person/s. This enables numerous applications in Security, Retail, Healthcare, Geriatric care, Fitness, Sports domains. Coupled with Augmented/Mixed Reality, we can transpose a human into a virtual world thus opening up newer opportunities and experiences in Fashion retail, Entertainment, Advertising and Gaming. Along with gesture recognition you can interact with the virtual world.

What is Myriad NCS?

If you have not heard of Intel’s Neural Compute Stick, it is a small device that plugs in via USB port and runs deep neural networks. Think of it as a USB graphics card that is optimised to run certain deep learning frameworks and models. Being a USB device, it can be run on an edge computing device such as a Raspberry Pi. It is low powered and comparatively small. These points make it a very good choice to run machine learning models on edge. If you are looking for something more embedded you can look at the VPUs from Intel.

OpenVINO provided OpenPose Model

OpenVINO provides a set of pre-trained models which can be run on Movidius NCS without having to go through the conversion process. One of the pre-trained models is human-pose-estimation. It is a multi-person model, based on MobileNet V1 and trained using caffe framework.

This model is a larger architecture based on OpenPose. The complexity is 15GFlops with 42.8% average precision on COCO dataset. The high complexity of the model is a bottleneck, rendering the option unusable on edge for real time detection. During our benchmarks, the model gave 2FPS on Movidius NCS 1. However, the accuracy was higher than PoseNet.

Tensorflow JS Posenet Model

Google has released a freely available, pre-trained model for pose estimation in browser, it is called PoseNet. You can refer to this blog post to know more about the model and its architecture.

In brief, the model is based on MobileNet V1 and is trained to detect single-person or multi-person poses. The model is optimised to run on Tensorflow JS which means it is light enough to run in a web browser.

Here is an overview of what we are going to do:

Convert Tensorflow JS model to a normal Tensorflow model
Install OpenVINO
Convert Tensorflow model to OpenVINO supported format
Run the model on Movidius NCS

Convert tfjs to Tensorflow

You can take one of the following 3 ways to get a .pb file:

Download the files generated by us: click here to download
Convert it yourself using tfjs-converter
Use this repo, which downloads and converts the tfjs models for you

The simplest way is to download the ones we have given. That way you don’t have to install extra stuff on your computer and worry about the process of conversion.

As you will notice, there are 3 important files:

model-mobilenet_v1_050.pb
model-mobilenet_v1_075.pb
model-mobilenet_v1_100.pb

These files refer to different version of MobileNet on which the pose estimator has been trained. To simplify, 050 is the fastest with low accuracy, 075 has more accuracy but is slower than 050. Lastly, 100 is the slowest but the most accurate among the three.

Which one should you choose? Keep reading, we are going to evaluate which model gives the best trade-off of accuracy and speed soon!

Install OpenVINO

To be able to run the model on Movidius NCS, we are going to use Intel’s distribution of OpenVINO toolkit. OpenVINO can be installed on Linux, Windows & Raspbian OS. You can follow the official instructions to install the toolkit. We have installed the toolkit on Ubuntu 16.04 to convert the model, and used Raspbian to run the model.

Step 1:

Install OpenVINO toolkit on your Linux machine. Keep in mind that you won’t be able to convert a tensorflow model to OpenVINO supported format on a Raspberry Pi, so this installation is a must (or install it on Windows).

Step 2:

Install OpenVINO toolkit on Raspbian. Raspbian installation of the toolkit only has inference engine. Which means you cannot convert your tensorflow (or caffe, MXNet) models to Intermediate Representation supported by OpenVINO, you will only be able to run inference on already converted models.

Next, we are going to:

Convert tensorflow model to Intermediate Representation on a Linux machine
Run inference on Raspberry Pi

Convert Tensorflow Model to OpenVINO Intermediate Representation

Intermediate Representation (IR) of a model is a file format recognised by OpenVINO toolkit, which is optimised to run on edge computing devices such as Movidius NCS.

Run the following command in your terminal:

python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo.py \
        --input_model ~/Downloads/posenet_tensorflow_models/model-mobilenet_v1_075.pb \
        --framework tf \
        -o ~/posenet/ \
        --input image \
        --input_shape [1,224,224,3] \
        --output "offset_2,displacement_fwd_2,displacement_bwd_2,heatmap" \
        --data_type FP16

This will give you two files: model-mobilenet_v1_075.mapping and model-mobilenet_v1_075.xml. These files are necessary to run inference on Movidius NCS.

You can replace — input_model with other versions of PoseNet (050 and 100) to get Intermediate Representations.

Transfer the two files on your Raspberry Pi and continue to the next step!

Running Inference on Raspberry Pi

Assuming you have installed OpenVINO toolkit on your Raspberry Pi and have transferred .mapping and .xml files, it is time to clone the repository .

The repository contains code to run benchmarks on Movidius. The code does not perform any image post processing to get proper benchmarks and to keep things simple. You can write OpenCV layer to render the key points on top of your input image.

Make sure your Movidius NCS is attached to the Raspberry Pi. Download an image of a person from the Internet and save it. Let’s call the downloaded image’s location $IMAGE_PATH. Next, move your model-mobilenet_v1_075.xml and model-mobilenet_v1_075.mapping files to the repository’s root.

Execute the following command in your terminal to run inference on Raspberry Pi:

python3 run_inference.py -m ./model-mobilenet_v1_075.xml -d MYRIAD -i $IMAGE_PATH

Results

FPS comparison of different mobilenet models

The smallest model performs the fastest, with 42 frames per second! Check out the videos to understand how accurate each of them are:

We recommend you use 075 version, because 30 FPS is smooth enough for human eyes to consider it real time, and the accuracy is acceptable too for many use cases. However, you might want to consider another version depending upon your use case.

References:

September 12, 2019September 26, 2019

Why we chose Rust over Lua for our project?

Choosing a programming language(s) for a new product is an important strategic decision. It influences a lot of things and has long-term implications for hiring, culture and even the viability of a product.

But the first things to be considered is whether the language is viable for the particular problem statement you are trying to solve.

Important questions are:

How suitable is the language for your particular use case?
Will it perform up to the mark?
Will it run on the targeted platform(s)?

These should be the primary questions. But there are more things that might influence your decision. Like:

How choosing a particular language will influence your turnaround time from idea to reality?
What are the cost benefits of using a particular language?
How easy will be it to solve new problems that you might stumble along the way?

Keeping these questions in mind, this article will try to explain our reasoning behind choosing Rust for our new product.

Use Case

Our problem statement was to use an edge device that can process data from different sources in real-time and create a knowledge graph, therefore the language we choose must be fast to allow minimum real-time latency and use limited resources of an SoC device.

Performance

Comparing the cross-language performance of real applications is tricky. We usually don’t have the same expertise in multiple languages and performance is much more influenced by the algorithms and data structures the programmer choose to use. But as the benchmarks above show, it is generally believed that Rust performs on par with C++. And performs much better than other interpreter or JIT based languages such as Lua or Python.

Concurrency

As described in the use case above, we wanted to process data in real-time from multiple sensors. Our target platform, SoC devices, use ARM-based CPUs and generally have 4+ cores. We wanted to utilize all CPU cores, that means having multithreading support was important.

Lua does not have native multithreading support, there are 3rd party workarounds but the performance and reliability of those are questionable. Rust, on the other hand, has built-in support for multi-threading and Its ownership, borrowing rules help us write very safe concurrent code.

Memory Safety

Dynamically typed languages give you a lot of flexibility. Type changes do not need manual propagation through your program. It also gives more mental flexibility, as you can think more in terms of transformations, operations, and algorithms. Flexibility lets you move faster, change things quickly, iterate at a faster velocity. But it comes with a cost. It’s a lot easier to miss potential problems and these problems are generally very hard to debug. Plus these features generally comes with a performance penalty.

On another hand in a statically typed language, a large number of errors are caught in the early stage of the development process, and static typing usually results in compiled code that executes more quickly because when the compiler knows the exact data types that are in use, it can produce optimized machine code. Static types also serve as documentation.

Rust goes above and beyond these points. Rust’s very strict and pedantic compiler checks each and every variable you use and every memory address you reference. It avoids possible data race conditions and informs about undefined behavior.

The right part of the chart above shows concurrency and memory safety issues. These are the most complex and unpredictable classes of errors and are fundamentally impossible to get in the safe subset of Rust. Moreover, all these type related bugs are dangerous and result in a variety of security vulnerabilities.

Type safety is one of the Rust’s biggest selling point and is the reason Rust topped as most loved language for 3 consecutive years in StackOverflow Surveys.

The way Rust achieved this feat is by using the concept of ownership of a variable. In Rust, every value has an “owning scope,” and passing or returning a value means transferring ownership to a new scope. You lend out the access to the functions you call, that’s called “borrowing”. Rust ensures that these leases do not outlive the object being borrowed. This not only makes it very type safe but also helps you tackle concurrency head-on because memory safety and concurrency bugs often come down to code accessing data when it shouldn’t.

Developer Experience

Rust has a steep learning curve. Most of it is due to the “ownership” & “borrowing” concepts we discussed above. Which makes Rust difficult and more time consuming than garbage collected languages like Lua or Python. It requires one to be very aware of basic computing principles regarding memory allocation and concurrency, and It requires you to keep these principles in mind while implementing, this should be the case for any language, but in Rust particularly, you are explicitly forced by compiler to write optimum memory-safe code.

Yet Rust has a lot of features and conveniences that almost make it feel like a high-level language despite the fact that you’re doing things like manual memory management that you do in C++. And Rust has a lot of abstractions that make it not feel like manual memory management anymore.

Low-level control and high-level safety promises developers far more control over performance without having to take on the burden of learning C/C++, or assume the risks of getting it wrong.

Conclusion

When you want to implement a high-performance concurrent system with low resource footprint, the choice of programming languages is limited. Interpreter based languages tend to perform poorly in high concurrency & low resource environments. System programming languages are the idle candidate for such use cases.

C/C++ is the holy grail of systems programming languages. But there’s a reason C & C++ are also one to most dreaded languages in StackOverflow Surveys. For newer programmer coming out of other higher level languages, approaching C/C++ is hard. The learning curve is very steep. There are approximately 74 different build systems and a patchwork framework of 28 different package managers, most of which only support a certain platform/environment and are useless outside of it. After 30+ years of evolution, new programmers have too much thrown at them.

Rust on other have is comparatively easier to approach, has a sizable community, does not come with decades of technical dept, yet provides comparative performance. Memory safety & easier concurrency are just added benefits.

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August 27, 2019September 26, 2019

Car or Not a Car

Lessons from Fine Tuning a Convolutional Binary Classifier

Fine tuning has been shown to be very effective in certain types of neural net based tasks such as image classification. Depending upon the dataset used to train the original model, the fine-tuned model can achieve a higher degree of accuracy with comparatively less data. Therefore, we have chosen to fine tune ResNet50 pre-trained on the ImageNet dataset provided by Google.

We are going to explore ways to train a neural network to detect cars, and optimise the model to achieve high accuracy. In technical terms, we are going to train a binary classifier which performs well under real-world conditions.

Taken in a village Near Jaipur (Rajasthan, India) by Sanjay Kattimani http://sanjay-explores.blogspot.com

There are two possible approaches to train such a network:

Train from scratch
Fine-tune an existing network

To train from scratch, we need a lot of data — millions of positive and negative examples. The process doesn’t end at data acquisition. One has to spend a lot of time cleaning the data and making sure it contains enough examples of real world situations that the model is going to encounter practically. The feasibility of the task is directly determined by the background knowledge and time required to implement that.

Basic Setup

There are certain requisites that are going to be used throughout the exploration:

Datasets
a.Standford Cars for car images
b. Caltech256 for non-car images
Base Network
ResNet — arXiv — fine-tuned on ImageNet
Framework and APIs
a. TensorFlow
b. TF Keras API
Hardware
a. Intel i7 6th gen
b. Nvidia GTX1080 with 8GB VRAM
c. System RAM 16GB DDR4

Experiment 1

To start with a simple approach, we take ResNet50 without the top layer and add a fully connected (dense) layer on top of it. The dense layer contains 32 neurons which are activated with sigmoid activator. This gives approximately 65,000 trainable parameters which are plenty for the task at hand.

We then add the final output layer having a single neuron with sigmoid activation. This layer has a single neuron because we are performing binary classification. The neuron will output real values ranging from 0 to 1.

Data Preparation

We are randomly sampling 50% of images as the training dataset, 30% as validation and 20% as test sets. Although there is a huge gap between the number of car and non-car images in the training set, it should not skew our process too much because the datasets are comparatively clean and reliable.

Hyper-parameters

Results

As a trial run, we trained for one epoch. The graphs below illustrate that the model starts at high accuracy, and reaches near-perfect performance within the first epoch. The loss goes down as well.

However, validation accuracy does not seem very good compared to the training round, and neither does validation loss.

So, we ran for 4 epochs and were left with the following results:

Validation accuracy and validation loss for four epochs

The model performs relatively well, except for the high degree of separation between training and validation losses.

Experiment 2

We decided to keep the model architecture the same as the one we used in the first experiment, using the same ResNet50 without the top layer and adding a fully connected (dense) layer on top of it containing 32 neurons activated with sigmoid activator.

Data Preparation

This is where the problem lay in the previous experiment. The train/validation/test data splits were random. The hypothesis was that the randomness has added more images of some cars, and too little of others, causing the model to be biased.

So, we took the splits as given by the Cars dataset and added 3000 more images by scraping the good old Web.

Hyper-parameters

Results

These results signify a substantial improvement in the validation accuracy when compared to the previous experiment.

Even though the accuracy matches fairly well, there is a big difference between the training loss and the validation loss.

This network seems more stable than the previous one. The only observable difference is that of new data splits.

Experiment 3

Here we add an extra dropout layer which provides a 30% chance that a neuron will be dropped out of the training pass. The dropout layer has been known to normalize models, to prevent possible biases caused by interdependence of neurons.

Since we have a comparatively huge pre-trained network and smaller trainable network, we could add more dense layers to see the effects. We did that and the model ended up achieving saturation in fewer epochs. No other improvements were observed.

Data Preparation

Just like in experiment 2, the default train/validation splits are taken.

Hyper-parameters

Here, we have run the model on a single learning rate but the value can be experimented with. We will talk about the effects of batch size on this network in the results section.

Results

The results here are with the batch size of 32. As seen, in 3 epochs the network seems to saturate (although it might be a bit premature to judge this).

At the same time validation accuracy and loss also seem to be performing well.

So, we increase the batch size to 128 hoping it would help the network find a better local minima and thereby giving a better overall performance. Here is what happened:

Epoch Accuracy and Loss for batch size of 128

Validation Accuracy and Loss for batch size of 128

The model now performs reasonably well on both training and validation sets. The losses between training and validation runs are not too far apart either.

Model Drawbacks

Obviously, the model is not one hundred percent accurate. It does provide certain failed classifications as a result.

Conclusion

When we ran this model on the testing dataset, it failed on only 7 images out of car + non-car sets. This is a very high degree of performance accuracy and closer to production usage.

In conclusion, we can safely assert that dataset splits are crucial. Rigorous evaluations and experimentation with various hyper-parameters give us a better idea of the network. We should also think about modifying the original architecture based on the evidence provided by the various hyper-parameters.

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