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.

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 multiple Neural Compute Sticks with OpenVINO


As I had discovered, one Neural Compute Stick 2 (NCS 2) has pretty decent throughput. The question then is: what happens if you connect more than one of these to the same machine? I only have one NCS 2 and one of the older NCS devices to test this out but that combination worked ok with some tuning. OpenVINO manages allocation of requests to physical devices so there is no explicit way for this to be controlled via the API. However, it appears that multiple SPEs on the same node can be supported as then the NCSs are divided up between the SPEs. A reset error message is typically emitted but then everything seems to work fine.

To get the best performance, I ran in async mode using multiple ExecutableNetwork/InferRequest pairs, with the actual number being configurable from the rtaiDesigner GUI. In this case, 5 pairs gave the best results. The throughput is around 18 frames per second running ssd_mobilenet_v2_coco object detection.


Using one NCS at a time, the NCS 2 was able to process 12 frames per second (versus 9 frames per second in synchronous mode using the original SPE code) while the older NCS was able to process 6 frames per second,  suggesting that both were being fully utilized.

Now I need to get a second NCS 2…

SSD object detection using the Neural Compute Stick 2 now has its own rt-ai stream processing element


Turned out to be pretty easy to integrate the ssd_mobilenet_v2_coco model compiled for the Intel NCS 2 into rt-ai Edge. Since it doesn’t use the GPU, I was able to run this and the YOLOv3 SPE on the same machine which is kind of amusing – one YOLOv3 instance tends to chew up most of the GPU memory, unfortunately, so the GPU can’t be shared. I would have liked to have run YOLOv3 on the NCS 2 for direct comparison but could not. The screen capture above shows the MediaView SPE output for both detectors running on the same 1280 x 720 video stream.


This is the design and it is showing the throughput of each detection SPE – 14 fps for the GTX 1080 ti YOLO and 9 fps for the NCS 2 based SSD. Not exactly a fair comparison, however, but still interesting. It would be much better if I had the same model running using a GPU of course. Right now, the GPU-based SPE that can run ssd_mobilenet_v2_coco (and similar models) is Python based and that (not surprisingly) runs a fair bit slower than the compiled C++ versions I am using here.

ssd_mobilenet_v2_coco running on the Intel Neural Compute Stick 2


I had more luck running the ssd_mobilenet_v2_coco model from the TensorFlow model detection zoo on the NCS 2 than I did with YOLOv3. To convert from the .pb file to the OpenVINO-friendly files I used:

python3 mo_tf.py --input_model ssdmv2.pb --tensorflow_use_custom_operations_config ./extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config ssdmv2_pipeline.config --data_type FP16

In this case, I had renamed the frozen_instance_graph.pb from the download as ssdmv2.pb and renamed the pipeline.config file from the download as ssdmv2_pipeline.config. The screen capture above shows the object_detection_demo_ssd_async demo app running with the NCS 2. I didn’t sort out the labels for this test which is why it is just displaying numbers for the detected objects.

I also tried this using the CPU (using –data_type FP32) with this result:

It is worth noting that the video was running at 1920 x 1080 which is a significant challenge for just about anything. The CPU (an i7 5820K) is obviously a fair bit faster than the NCS 2 but a real advantage is the small physical footprint, low price, low power and CPU offload that the Myriad X VPU in the NCS 2 offers.