Combining TrueDepth, remote OpenPose inference and local depth map processing to generate spatial 3D pose coordinates


The problem with depth maps for video is that the depth data is very large and can’t be compressed easily. I had previously run OpenPose at 30 FPS using an iPad Pro and remote inference but that was just for the standard OpenPose (x, y) coordinate output. There’s no way that 30 FPS could be achieved by sending out TrueDepth depth maps with each frame. Instead, the depth processing has to be handled locally on the iPad – the depth map never leaves the device.

The screen capture above shows the system running at 30 FPS. I had to turn a lot of lights on in the office – the frame rate from the iPad camera will drop below 30 FPS if it is too dark which messes up the data!


This is the design. It is the triple scaled OpenPoseGPU design used previously. iOSOpenPose connects to the Conductor via a websocket connection that is used to send images to and receive processed images from the pipeline.

One issue is that each image frame has its own depth map and that’s the one that has to be used to convert the OpenPose (x, y) coordinates into spatial (x, y, z) distances. The solution, in a new app called iOSOpenPose, is to cache the depth maps locally and re-associate them with the processed images when they return. Each image and depth frame is marked with a unique incrementing index to assist with this. Incidentally, this is why I love using JSON for this kind of work – it is possible to add non-standard fields at any point and they will be carried transparently to their destination.

Empirically with my current setup, there is a six frame processing lag which is not too bad. It would probably be better with the dual scaled pipeline, two node design that more easily handles 30 FPS but I did not try that. Another issue is that the processing pipeline can validly lose image frames if it can’t keep up with the offered rate. The depth map cache management software has to take care of all of the nasty details like this and other real-world effects.

Generating 3D spatial coordinates from OpenPose with the help of the Stereolabs ZED camera


OpenPose does a great job of estimating the (x, y) coordinates of body points. However, in many situations, the spatial (3D) coordinates of the body joints is what’s required. To do that, the z coordinate has to be provided in some way. There are two common ways of doing that: using multiple cameras or using a depth camera. In this case, I chose using RGBD data from a StereoLabs ZED camera. An example of the result is shown in the screen capture above and another below. Coordinates are in units of meters.

The (x, y) 2D coordinates within the image (generated by OpenPose) along with the depth information at that (x, y) point in the image are used to calculate a spatial (sx, sy, sz) coordinate with origin at the camera and defined by the camera’s orientation. The important thing is that the spatial relationship between the joints is then trivial to calculate. This can be used by downstream inference blocks to discriminate higher level motions.

Incidentally I don’t have a leprechaun sitting on my computers to the right of the first screen capture – OpenPose was picking up my reflection in the window as another person.

The ZED is able to produce a depth map or point cloud but the depth map is more practical in this case as it necessary to transmit the data between processes (possibly on different machines). Even so, it is large and difficult to compress. The trick is to extract the meaningful data and then discard the depth information as soon as possible! The ZED camera also sends along the calibrated horizontal and vertical fields of view as this is essential to constructing (sx, sy, sz) from (x, y) and depth. Since the ZED doesn’t seem to produce a depth value for every pixel, the code samples an area around the (x, y) coordinate to evaluate a depth figure. If it fails to do this, the spatial coordinate is returned as (0, 0, 0).


This is the design I ended up using. Basically a dual OpenPose pipeline with scaler as for standard OpenPose. It averaged around 16 FPS with 1280 x 720 images (24 FPS with VGA images) using JPEG for the image part and raw depth map for the depth part. Using just one pipeline achieved about 13 FPS so the speed up from the second pipeline was disappointing. I expect that this was largely due to the communications overhead of moving the depth map around between nodes. Better network interfaces might improve this.

Real time OpenPose on an iPad…with the help of remote inference and rendering

I wanted to use the front camera of an iPad to act as the input to OpenPose so that I could track pose in real time with the original idea being to leverage CoreML to run pose estimation on the device. There are a few iOS implementations of OpenPose (such as this one) but they are really designed for offline processing as they are pretty slow. I did try a different pose estimator that runs in real time on my iPad Pro but the estimation is not as good as OpenPose.

So the question was how to run iPad OpenPose in real time in some way – compromise was necessary! I do have an OpenPose SPE as part of rt-ai Edge that runs very nicely so an obvious solution was to run rt-ai Edge OpenPose on a server and just use the iPad as an input and output device. The nice plus of this new iOS app called iOSEdgeRemote is that it really doesn’t care what kind of remote processing is being used. Frames from the camera are sent to an rt-ai Edge Conductor connected to an OpenPose pipeline.

The rt-ai Edge design for this test is shown above. The pipeline optionally annotates the video and returns that and the pose metadata to the iPad for display. However, the pipeline could be doing anything provided it returns some sort of video back to the iPad.

The results are show in the screen captures above. Using a GTX 1080 ti GPU, I was getting around 19fps with just body pose processing turned on and around 9fps with face pose also turned on. Latency is not noticeable with body pose estimation and even with face pose estimation turned on it is entirely usable.

Remote inference and rendering has a lot of advantages over trying to squeeze everything into the iPad and use CoreML  for inference if there is a low latency server available – 5G communications is an obvious enabler of this kind of remote inference and rendering in a wide variety of situations. Intrinsic performance of the iPad is also far less important as it is not doing anything too difficult and leaves lots of resource for other processing. The previous Unity/ARKit object detector uses a similar idea but does use more iPad resources and is not general purpose. If Unity and ARKit aren’t needed, iOSEdgeRemote with remote inference and rendering is a very powerful system.

Another nice aspect of this is that I believe that future mixed reality headset will be very lightweight devices that avoid complex processing in the headset (unlike the HoloLens for example) or require cables to an external processor (unlike the Magic Leap One for example). The headset provides cameras, SLAM of some sort, displays and radios. All other complex processing will be performed remotely and video used to drive the displays. This might be the only way to enable MR headsets that can run for 8 hours or more without a recharge and be light enough (and run cool enough) to be worn for extended periods.

rt-ai Edge dynamic and adaptive parallel inference using the new Scaler SPE

One way to achieve higher video frame inference rates in situations where no state is maintained between frames is to split an incoming video stream across multiple inference pipelines. The new rt-ai Edge Scaler Stream Processing Element (SPE) does exactly that. The screen capture above shows the design and the real time performance information (in the windows on the right). The pipelines in this case are just single SPEs running single shot object detection on the Intel NCS 2. The CSSD SPE is able to process around 13 1280 x 720 frames per second by itself. Using the Scaler SPE to leverage two CSSD SPEs, each with one NCS 2 running on different nodes, the throughput has been doubled. In fact, performance should scale roughly linearly with the number of pipelines attached.

The Scaler SPE implements a health check function that determines the availability of pipelines at run time. Only pipelines that pass the health check are eligible to receive frames to be processed. In the example, Scaler can support eight pipelines but only two are connected (2 and 6) so only these pass the health check and receive frames. Frames from the In port are distributed across the active pipelines in a round robin fashion.

Pipelines are configured with a maximum to the number of in-flight frames in order to maximize pipeline throughput and minimize latency. Without multiple in-flight frames, CSSD performance would be roughly halved. In fact, pipelines can have different processing throughputs – the Scaler SPE automatically adjusts pipeline usage based on achieved throughput. Result messages may be received from pipelines out of sequence and the Scaler SPE ensures that the final output stream on the Out port has been reordered correctly.

The Scaler SPE can actually support any type of data (not just video) and any type of pipeline (not just inference) provided there is no retained state between messages. This makes it a very useful new building block.

Adding depth to DNN object detection with ARKit and Unity AR Foundation


Following on from the previous post, I thought that it would fun to try adding depth information to the detected objects using surface planes constructed by ARKit. The results are not at all bad. ARKit didn’t always detect the vertical planes correctly but horizontal ones seemed pretty reliable.  I just used Unity AR Foundation‘s ray casting function at the center of the detected object to get a depth indication. Of course this is really the distance to the nearest horizontal or vertical plane so it isn’t perfect.

In the end, there’s no replacement for mobile devices with proper depth sensing cameras. Even though Tango didn’t make it, it would be nice to think that real depth sensing could become mainstream one day.

Using edge inference to detect real world objects with Unity AR Foundation, ARKit and rt-ai Edge

The Unity AR Foundation provides a convenient high level way of utilizing ARCore and ARKit in order to implement mixed and augmented reality applications. I used it to implement an iPad app that could access an rt-ai Edge Composable Processing Pipeline (CPP) via the new Conductor Stream Processing Element (SPE). This is the CPP used to test Conductor:


The Conductor SPE provides a Websocket API to mobile devices and is able to pass data from the mobile device to the pipeline and then return the results of the CPP’s processing back to the mobile device. In this case, I am using the CYOLO SPE to perform object detection on the video stream from the mobile device’s camera. The output of the CYOLO SPE goes to three destinations – back to the Conductor, to a MediaView for display locally (for debug) and also to a PutManifold SPE for long term storage and off-line processing.

The iPad Unity app used to test this arrangement uses AR Foundation and ARKit for spatial management and convenient access to camera data. The AR Foundation is especially nice as, if you only need the subset of ARKit functionality currently available, you can do everything in the C# domain without having to get involved with Swift and/or Objective C and all that. The captured camera data is formatted as an rt-ai Edge message and sent via the Websocket API to the Conductor. The Conductor returns detection metadata to the iPad which then uses this to display the labelled detection frames in the Unity space.

Right now, the app draws a labelled frame at a constant distance of 1 meter from the camera to align with the detected object. However, an enhancement would be to use depth information (if there is any) so that the frame could be positioned at the correct depth. Or if that wasn’t useful, the frame label could include depth information.

This setup demonstrates that it is feasible for an XR app to offload inference to an edge compute system and process results in real time. This greatly reduces the load on the mobile device, pointing the way to lightweight, low power, head mounted XR devices that could last for a full workday without recharge. Performing inference on-device (with CoreML for example) is certainly a viable alternative, especially where privacy dictates that raw data (such as video) cannot leave the device. However, processing such data using an edge compute system is hardly the same as sending data out to a remote cloud so, in many cases, privacy requirements can still be satisfied using edge offload.

This particular setup does not require Orchestrator as the iPad test app can go directly to the Conductor, which is part of a statically allocated CPP. The next step to complete the architecture is to add in the Orchestrator interaction so that CPPs can be dynamically instantiated.

An rt-ai Edge architecture for scalable on-demand edge inference systems


Previous rt-ai Edge designs, such as the driveway monitor, are static in the sense that they just sit there, running 24/7. Another mode of operation is dynamic, where stream processing networks are created on demand and accessible via standard interfaces. This is appropriate for offloading inference from mobile devices in a sentient space for example. As users enter the space, apps on their mobile devices (XR headsets, tablets, phones etc) can access inference and other processing resources from the edge compute system supporting the space.

There are three main components in a dynamic rt-ai Edge system:

  • Composable Processing Pipeline (CPP). This is the dynamic analog of the static Stream Processing Network (SPN). A CPP is a set of Stream Processing Elements (SPEs) that has been designed using rtaiDesigner. The main difference between a CPP and an SPN is that, in general, the CPP contains no data sources or sinks: these are provided by the user app.
  • Conductor. The Conductor is responsible for managing an allocated resource session. User apps interact directly with the Conductor via a Websocket API while the Conductor maps data flowing on the Websocket API to and from the MQTT interfaces on the CPP(s) that have been allocated to that session.
  • Orchestrator. The Orchestrator manages the dynamic system. User apps interact with the Orchestrator to request resource. The Orchestrator allocates necessary CPP resources and creates a Conductor instance to act as the source and sink for the CPP(s). The user apps are then redirected to the Websocket API on the new Conductor instance at which point data can flow to and from the user. The Orchestrator is responsible for managing all of the rt-ai Edge nodes that have been allocated to the edge compute system, allocating CPPs to nodes dynamically based on available resources and hardware (e.g. GPU or embedded inference hardware).

The diagram above shows the idle state. The heart of this design is the Orchestrator as it directs all operations. When a user (via an app or browser) wants to use some edge resource, it uses the RESTful API of the Orchestrator to identify itself and define the details of the resources that it requires. The requested resources are then mapped to one or more CPP types. In this example, the Orchestrator maintains a hot pool of CPPs to minimize start up latency. Hot pool CPPs are instantiated but idle as they have no data sources. As the Orchestrator allocates CPPs from the pool, the Orchestrator creates new CPP instances to replace them. This is useful because inference SPEs can have startup times of several seconds. The hot pool hides this delay from the user. Note that the hot pool could consist of multiple types of CPPs that perform different functions – the Orchestrator just selects the correct type to satisfy the resource request. Alternatively, there could be a fixed set of CPP instances and users are just allocated to those. Or, CPPs can be instantiated on demand if startup latency is not an issue.

Once the Orchestrator has identified one or more CPPs to satisfy the resource request, it creates a Conductor instance for the request. The Conductor presents a Websocket API to the user while connecting into rt-ai Edge’s MQTT infrastructure to communicate with the CPPs. If there is only a single CPP involved, the input pin of the CPP is connected to the output pin of the Conductor and the input pin of the Conductor is connected to the output pin(s) of the CPP. If there is more than one CPP required, the CPPs are connected together as required (this can be an arbitrary graph, not just a pipeline) and the input and output pin(s) at the edges connected to the Conductor. Once this is all set up, the Orchestrator redirects the user app to the new Conductor instance and the session can begin as shown below:


As an example, suppose an AR headset user wants to identify and annotate objects in the real world using an AR overlay. In this case, the user app might request a CPP that performs the appropriate object detection and returns the box coordinates of the object and an identified label. The user app would stream the video feed from the AR headset to the Conductor using the Websocket connection. The Conductor would then pass the video frames on to the CPP. The output of the CPP would contain the detected object metadata that is passed via the Conductor onto the Websocket connection back to the user app for rendering.