Most IP cameras, including security and surveillance cameras, support RTSP H.264 streaming so it made sense to implement a compatible stream processing element (SPE) for rt-ai Edge. The design above is a simple test design. The video stream from the camera is converted into JPEG frames using GStreamer within the SPE and then passed to the DeepLabv3 SPE. The output from DeepLabv3 is then passed to a MediaView SPE for display.
I have a few ONVIF/RTSP cameras around the property and the screen capture above shows the results from one of these. There’s a car sitting in its field of view that’s picked out very nicely. I am using the DeepLabv3 SPE here in its masked image mode where the output frames just consist of recognized object images and nothing else.
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.
One of the goals of the rt-ai Edge system is that users of the system can use whatever device they have available to interact and extract value from it. Unity is a tremendous help given that Unity apps can be run on pretty much everything. The main task was integration with Manifold so that all apps can receive and interact with everything else in the system. Manifold currently supports Windows, UWP, Linux, Android and macOS. iOS is a notable absentee and will hopefully be added at some point in the future. However, I perceive Android support as more significant as it also leads to multiple MR headset support.
The screen shot above and video below show three instances of the rt-ai viewer apps running on Windows desktop, Windows Mixed Reality and Android interacting in a shared sentient space. Ok, so the avatars are rubbish (I call them Sad Robots) but that’s just a detail and can be improved later. The wall panels are receiving sensor and video data from ZeroSensors via an rt-ai Edge stream processing network while the light switch is operated via a home automation server and Insteon.
Sharing is mediated by a SharingServer that is part of Manifold. The SharingServer uses Manifold multicast and end to end services to implement scalable sharing while minimizing the load on each individual device. Ultimately, the SharingServer will also download the space definition file when the user enters a sentient space and also provide details of virtual objects that may have been placed in the space by other users. This allows a new user with a standard app to enter a space and quickly create a view of the sentient space consistent with existing users.
While this is all kind of fun, the more interesting thing is when this is combined with a HoloLens or similar MR headset. The MR headset user in a space would see any VR users in the space represented by their avatars. Likewise, VR users in a space would see avatars representing MR users in the space. The idea is to get as close to a telepresent experience for VR users as possible without very complex setups. It would be much nicer to use Holoportation but that would require every room in the space has a very complex and expensive setup which really isn’t the point. The idea is to make it very easy and low cost to implement an rt-ai Edge based sentient space.
Still lots to do of course. One big thing is audio. Another is representing interaction devices (pointers, motion controllers etc) to all users. Right now, each app just sends out the camera transform to the SharingServer which then distributes this to all other users. This will be extended to include PCM audio chunks and transforms for interaction devices so that everyone will be able to create a meaningful scene. Each user will receive the audio stream from every other user. The reason for this is that then each individual audio stream can be attached to the avatar for each user giving a spatialized sound effect using Unity capabilities (that’s the hope anyway). Another very important thing is that the apps work differently if they are running on VR type devices or AR/MR type devices. In the latter case, the walls and related objects are not drawn and just the colliders instantiated although virtual objects and avatars will be visible. Obviously AR/MR users want to see the real walls, light switches etc, not the virtual representations. However, they will still be able to interact in exactly the same way as a VR user.
Th 3DView app I mentioned in a previous post is moving forward nicely. The screen capture shows the app capturing real time from four ZeroSensors, with the real time data coming from an rt-ai Edge stream processing network via Manifold. The app creates a video window and sensor display panel for each physical device and then updates the data whenever new messages are received from the ZeroSensor.
This is the rt-ai Edge part of the design. All the blocks are synth modules to speed the design replication. The four ZeroManifoldSynth modules each contain two PutManifold stream processing elements (SPEs) to inject the video and sensor streams into the Manifold. The ZeroSynth modules contain the video and sensor capture SPEs. The ZeroManifoldSynth modules all run on the default node while the ZeroSynth modules run directly on the ZeroSensors themselves. As always with rt-ai Edge, deployment of new designs or design changes is a one click action making this kind of distributed system development much more pleasant.
The Unity graphics elements are basic as I take the standard programmer’s view of Unity graphics elements: they can always be upgraded later by somebody with artistic talent but the key is the underlying functionality. The next step moving forward is to hang these displays (and other much more interesting elements) on the walls of a 3D model of the sentient space. Ultimately the idea is that people can walk through the sentient space using AR headsets and see the elements persistently positioned in the sentient space. In addition, users of the sentient space will be able to instantiate and position elements themselves and also interact with them.
Even more interesting than that is the ability for the sentient space to autonomously instantiate elements in the space based on perceived user actions. This is really the goal of the sentient space concept – to have the sentient space work with the occupants in a natural way (apart from needing an AR headset of course!).
For the moment, I am going to develop this in VR rather than AR. The HoloLens is the only available AR device that can support the level of persistence required but I’d rather wait for the rumored HoloLens 2 or the Magic Leap One (assuming it has the required multi-room persistence capability).
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.
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.