AIY Vision Kit + MobileNet+SSD: a smart camera for rt-ai Edge

I decided that it would be fun to try out a Google AIY Vision Kit as a sort of warm-up for the potentially much more significant Edge TPU.

The Vision Kit is basically the same configuration as the ZeroSensor camera except with an extra board in the camera path that can perform inference on the captured images. The kit comes with some frozen graphs that can be used to detect a few things but I thought it would be interesting to try training a MobileNet SSD network with the Pascal VOC 2012 training data which can identify 20 different objects. The instructions for how to do this are here.

Once that was all running, the next step was to integrate it with rt-ai Edge. It’s pretty similar to the earlier full-blown TensorFlow version so it didn’t take too long to get working.

The design is much the same as usual except with the new VisionKit object detection SPE instead of TFObjectDetect or Deeplab. Note that the PiCam and VisionKit SPEs are running on the AIY Vision Kit, whereas the MediaView SPE is running on a desktop.

This is the output from the MediaView SPE. The metadata has been formatted to look exactly the same as the previous TensorFlow detector so that they can be used interchangeably in stream processing networks. I am getting about 2 fps with 640 x 360 images which is actually better than I expected.

Completed ZeroSensors all ready for long term data collection

Finally this is a ZeroSensor all ready to go into full time service, capturing video, audio and environmental data. The goal is to use this data, and that from other cameras around the space, as training data for machine learning systems.

One specific goal is to create an anomaly detector with minimal supervision. As much as possible, it will learn from experience. This is kind of tricky as it requires detection of unknown length sequences depending on the circumstances. I am intrigued by the ideas behind the Universal Translator but not sure how much could carry over to this application. This paper reviews some of the techniques usually applied, at least for video processing. The situation here is a little different as there are quite different types of features involved. My plan is to preprocess video and audio to recognize salient features (using object detection or whatever) and then input these features, along with environmental sensor data, in the form of uniform time-slotted data sets to the anomaly detector. This doesn’t help with detecting the length of an interesting sequence – that’s the fun part of the project.

Integrating TensorFlow object detection into rt-ai Edge

I have been using DeepLabv3 for a while now for object detection but I thought it would be interesting to try some examples from the TensorFlow object detection repo. I now have an rt-ai Edge stream processing element that is based on the Jupyter notebook example in the repo. Presumably this will work with any of the models in the model zoo although I am just using the default one for now.

As you can see from the preview capture above (apart from the nasty looking grass on the left) it picks out the car happily, although not with a great confidence level. Maybe it doesn’t like the elevated camera position or the car is a bit too far away or a difficult pose – I will need to do some more experiments. With the preview display on (using PyGame) I am only getting 1 fps with 1280 x 720 frames from the camera which is a little disappointing. However, with preview turned off (the normal production mode anyway), I am getting over 15fps which is entirely adequate.

The capture above shows the raw image along with the object recognition data in the form of metadata rather than drawn on the image. This is actually pretty useful for both real-time and offline processing (such as a machine learning run). Capturing the original image does have the advantage that alternate object detectors could be run at any time, at the expense of having to store more data. Real-time actions can be based on the metadata and the raw image just discarded.

Anyway, definitely a work in progress. It will be interesting to see how it compares with the DeepLabv3 version as the implementation gets more efficient. What’s nice is that it is trivial to swap out one object detector for another or run them in parallel in order to run tests. Just takes a few seconds with the rtaiDesigner GUI.

An rt-xr SpaceObjects tour de force

rt-xr SpaceObjects are now working very nicely. It’s easy to create, configure and delete SpaceObjects as needed using the menu switch which has been placed just above the light switch in my office model above.

The video below shows all of this in operation.

The typical process is to instantiate an object, place and size it and then attach it to a Manifold stream if it is a Proxy Object. Persistence, sharing and collaboration works for all relevant SpaceObjects across the supported platforms (Windows and macOS desktop, Windows MR, Android and iOS).

This is a good place to leave rt-xr for the moment while I wait for the arrival of some sort of AR headset in order to support local users of an rt-xr enhanced sentient space. Unfortunately, Magic Leap won’t deliver to my zip code (sigh) so that’s that for the moment. Lots of teasers about the HoloLens 2 right now and this might be the best way to go…eventually.

Now the focus moves back to rt-ai Edge. While this is working pretty well, it needs to have a few bugs fixed and also add some production modes (such as auto-starting SPNs when server nodes are started). Then begins the process of data collection for machine learning. ZeroSensors will collect data from each monitored room and this will be saved by ManifoldStore for later use. The idea is to classify normal and abnormal situations and also to be proactive in responding to the needs of occupants of the sentient space.

Using blockchain technology to create verifiable sensor records and detect fakes

These days, machine learning techniques have led to the ability to create very realistic but fake video and audio that can be tough to distinguish from the real thing. The video above shows a very interesting example of this capability. The problem with this technology is that it will become impossible to determine if anything is genuine at all. What’s needed is some verification that a video of someone (for example) really is that person. Blockchain technology would seem to provide a solution for this.

Many years ago I was working on a digital watermarking-based system for detecting tampering in video records. Essentially, this embedded error-correcting codes in each frame that could be used to determine if any region of a frame had been modified after the digital watermark had been added. Cameras would add the digital watermark at source, limiting the opportunity for modification prior to watermarking.

One problem with this is that it worked on a frame by frame basis but didn’t ensure the integrity of an entire sequence. In theory this could be done with temporally distributed watermarks but blockchain technology provides a very nice alternative.

A simple strategy would be to have the sensor (camera, microphone, motion detector, whatever) create a hash for each unit of data (video frame, chunk of audio etc) and add this to a blockchain. Then a review app could create new hashes from the sensor data itself (stored elsewhere) and compare them to those in the blockchain. It could also determine that the account owner or device is who or what it is supposed to be in order to avoid spoofing. It’s easy to envisage an Etherium smart contract being the basis of such a system.

One issue with this is the potential rate at which hashes need to be added to the blockchain. This rate could be reduce by collecting more data (e.g. accumulating one second’s worth of data to generate one hash) or creating a hash of hashes at an appropriate rate. The only downside to this is losing temporal resolution of where changes have been made.

It’s worth considering the effects of lossy compression. Obviously if a stream is uncompressed or only uses lossless compression, watermarking and hash generation can be done at a very early stage. Watermarking of video is designed to withstand compression so that can still be done at a very early stage, even with lossy compression. The hash has to be be bit-accurate with the stream as stored on the video storage medium though so the hash must be computed after lossy compression.

It seems as though this blockchain concept could definitely be made to work and possibly combined with the digital watermarking technique in the case of video to provide temporal and spatial resolution of tampering. I am sure that variations of this concept are out there already or being developed and maybe, one day, it will be possible for anybody to check if a video of a well-known person is real or fake.

The ZeroSensor – a sentient space point of presence

One application for rt-ai Edge is ubiquitous sensing leading to sentient spaces – spaces that can interact with people moving through and provide useful functionality, whether learned or programmed. A step on the road to that is the ZeroSensor, four prototypes of which are shown in the photo. Each ZeroSensor consists of a Raspberry Pi Zero W, a Pi camera module v2, an Adafruit BME 680 breakout and an Adafruit TSL2561 breakout. The combination gives a video stream and a sensor stream with light, temperature, pressure, humidity and air quality values. The video stream can be used to derive motion sensing and identification while the other sensors provide a general idea of conditions in the space. Notably missing is audio. Microphone support would be useful for general sensing and I might add that in real devices. A 3D printable case design is underway in order to allow wide-scale deployment.

Voice-based interaction is a powerful way for users to interact with sentient spaces. However, it is assumed that people who want to interact are using an AR headset of some sort which itself provides the audio I/O capabilities. Gesture input would be possible via the ZeroSensor’s camera. For privacy reasons video would not be viewed directly or stored but just used as a source of activity data and interaction.

This is the simple rt-ai design used to test the ZeroSensors. The ZeroSynth modules are rt-ai Edge synth modules that contain SPEs that interface with the ZeroSensor’s hardware and generate a video stream and a sensor data stream. An instance of a video viewer and sensor viewer are connected to each ZeroSynth module.

This is the result of running the ZeroSensor test design, showing a video and sensor window for each ZeroSensor. The cameras are staring at the ceiling because the four sensors were on a table. When the correct case is available, they will be deployed in the corners of rooms in the space.

Scaling embedded edge inference with rt-ai Edge synth modules

Now that edge devices with embedded inference support are starting to appear, there’s a need for scalable deployment of software and configuration data to these devices. rt-ai Edge can address this scaling requirement using synth modules. Synth modules are composite elements in a stream processing network (SPN) that combine simpler stream processing elements (SPEs) into more complex structures. The idea is that a synth module can be created that contains the SPEs required for a specific type of embedded edge inference device. This synth module can then be deployed, configured and managed for all instances of this type of edge inference device very easily using the rtaiDesigner tool.

The screen capture above is an example of the output from an SPN that includes two differently configured DeepLab v3+ instances along with associated video and audio capture SPEs. The top level SPN looks like this:

There are two synth modules in the design, both instances of the same underlying synth module:

This simple synth module consists of a video capture SPE, an audio capture SPE and the DeepLab v3+ SPE.

As with standard SPEs, synth modules can be allocated to any node in the rt-ai Edge network. The only limitation at present is that all SPEs in an instance of a synth module must run on the same node. This will be relaxed at later date when automatic SPE placement based on available resources is implemented. A synth module can be instanced multiple times on the same node or different nodes as required. In this example, two instances of the same synth module were placed on the Default node.

Individual instances of a synth module can be configured in the top level design:

In this case, Synth0 is being configured. Note the tabs in the dialog. There is one tab for each SPE in the underlying synth module. SPE dialogs are auto-generated from a JSON spec in the SPE design directory. This makes it very easy to construct a combined dialog when SPEs are used in a synth module. Any design can be turned into a synth module just by pressing the Generate synth module button. The synth module then becomes available in the Add module dialog just like any other SPE.

As designs are completely regenerated every time the Generate design button is pressed, internal changes can be made to the synth module at any time and they will be reflected in top level designs the next time that they are generated.

Right now, synth module designs cannot include synth modules, only standard SPEs. If multi-level synth modules were required, it would be a small extension of the current implementation. For now, the ability to reproduce and configure a standard SPN subnetwork multiple times is sufficient to scale most edge inference applications.