GStreamer Motion Detection Bin Performance
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This section provides performance measurements for the rrmotiondetectionbin operating in various configurations. The following list describes different aspects of performance that were measured during the evaluation:
- Frame rate: This indicates the number of frames that can be processed per second.
- CPU utilization: This refers to the average percentage of CPU resources used by the pipeline.
- GPU utilization: This refers to the average percentage of GPU resources used by the pipeline.
To measure these aspects RidgeRun's perf element and NVIDIA application tegrastats were used. The measurements were taken using Jetpack 4.5 on a Jetson Xavier NX and configured in power mode ID 5.The results of these measurements can be found in the following subsections.
Contents
Algorithms Configurations
The table below show the results of testing different motion detection algorithms, blob detection method and changing the processing format.
- The motion detection algorithm was changed with the property motion_detector::motion and can take 2 values 'mog' or 'mog2'
- The blob detection method was changed with the property cuda-blob-detection and can take 2 values true or false, true to use GPU based blob detection or false to use CPU based blob detection.
- The processing format was changed with the property grayscale and can take 2 values: true or false, true to process the video in grayscale of false to process the value in RGBA.
The rest of the configurations uses the default values, noise reduction kernel size of 9 and sample frequency of 1.
4K Input Resolution
The measurements in this subsection were obtained from the following pipeline using a 4K input file:
gst-launch-1.0 filesrc location=/home/nvidia/street.mp4 ! qtdemux ! queue ! h264parse ! nvv4l2decoder ! queue ! rrmotiondetectionbin motion_detector::motion=$MOTION cuda-blob-detection$BLOB_METHOD grayscale=$PROCESS_FORMAT ! perf ! fakesink sync=true
The rrmotiondetectionbin configuration were changed to determine its performance with different settings, changed with the corresponding element's properties.
| Motion Detection Algorithm | Blob Detection Method | Processing Format | Framerate | CPU | GPU | |||
|---|---|---|---|---|---|---|---|---|
| mog | mog2 | CPU | GPU | Grayscale | RGBA | |||
| x | x | x | 29.91 | 16.769 | 88.397 | |||
| x | x | x | 22.098 | 13.617 | 79.529 | |||
| x | x | x | 29.921 | 15.963 | 75.115 | |||
| x | x | x | 23.509 | 13.938 | 83.529 | |||
| x | x | x | 23.097 | 12 | 90.971 | |||
1080p Input Resolution
The measurements in this subsection were obtained from the following pipeline using a 4K input file downscaled to 1080p
gst-launch-1.0 filesrc location=/home/nvidia/street.mp4 ! qtdemux ! queue ! h264parse ! nvv4l2decoder ! nvvidconv ! "video/x-raw(memory:NVMM),width=1920,height=1080" ! queue ! rrmotiondetectionbin motion_detector::motion=$MOTION cuda-blob-detection$BLOB_METHOD grayscale=$PROCESS_FORMAT ! perf print-cpu-load=true ! fakesink sync=true
The rrmotiondetectionbin configuration were changed to determine its performance with different settings, changed with the corresponding element's properties.
| Motion Detection Algorithm | Blob Detection Method | Processing Format | Framerate | CPU | GPU | |||
|---|---|---|---|---|---|---|---|---|
| mog | mog2 | CPU | GPU | Grayscale | RGBA | |||
| x | x | x | 29.927 | 10.423 | 48.391 | |||
| x | x | x | 29.921 | 8.74 | 51.807 | |||
| x | x | x | 29.925 | 10 | 29.615 | |||
| x | x | x | 29.921 | 10.423 | 40.629 | |||
| x | x | x | 29.928 | 10 | 51.08 | |||
Denoiser Kernel Size
To further analyze the impact of the denoiser we measured the performance for different kernel sizes, keeping the rest of configurations fixated as follows:
- Motion detection algorithm: MOG
- Blob detection: CPU
- Sample Frequency: 1
- Processing format: grayscale
4K Input Resolution
The following pipeline was used for these measurements:
gst-launch-1.0 filesrc location=/home/nvidia/street.mp4 ! qtdemux ! queue ! h264parse ! nvv4l2decoder ! queue ! rrmotiondetectionbin motion_detector::motion=mog motion_denoiser::size=$KERNEL_SIZE cuda-blob-detection=false ! perf print-cpu-load=true ! fakesink sync=true
| Denoiser Kernel Size | Framerate | CPU | GPU |
|---|---|---|---|
| 5 | 29.906 | 18.076 | 86.577 |
| 9 | 29.91 | 16.769 | 88.397 |
| 11 | 29.924 | 16.153 | 84.462 |
1080p Input Resolution
The following pipeline was used for these measurements:
gst-launch-1.0 filesrc location=/home/nvidia/street.mp4 ! qtdemux ! queue ! h264parse ! nvv4l2decoder ! nvvidconv ! "video/x-raw(memory:NVMM),width=1920,height=1080" ! queue ! rrmotiondetectionbin cuda-blob-detection=false motion_detector::motion=mog motion_denoiser::size=$KERNEL_SIZE ! perf print-cpu-load=true ! fakesink sync=true
| Denoiser Kernel Size | Framerate | CPU | GPU |
|---|---|---|---|
| 5 | 29.927 | 9.777 | 40.16 |
| 9 | 29.927 | 10.423 | 48.391 |
| 11 | 29.925 | 10 | 37.923 |
Motion Sampling Frequency
This subsections shows the performance changes caused by different sampling frequencies, keeping the rest of configurations fixated as follows:
- Motion detection algorithm: MOG
- Blob detection: CPU
- Denoise kernel size: 9
- Processing format: grayscale
4K Input Resolution
The following pipeline was used for these measurements:
gst-launch-1.0 filesrc location=/home/nvidia/street.mp4 ! qtdemux ! queue ! h264parse ! nvv4l2decoder ! queue ! rrmotiondetectionbin motion_detector::motion=mog motion_detector::sample-frequency=$FREQ motion_denoiser::size=9 cuda-blob-detection=false ! perf print-cpu-load=true ! fakesink sync=true
| Sample Frequency | Framerate | CPU | GPU |
|---|---|---|---|
| 1 | 29.91 | 16.769 | 88.397 |
| 2 | 30.232 | 11.44 | 56.292 |
| 4 | 30.448 | 8.72 | 40.739 |
1080p Input Resolution
The following pipeline was used for these measurements:
gst-launch-1.0 filesrc location=/home/nvidia/street.mp4 ! qtdemux ! queue ! h264parse ! nvv4l2decoder ! nvvidconv ! "video/x-raw(memory:NVMM),width=1920,height=1080" ! queue ! rrmotiondetectionbin cuda-blob-detection=false motion_detector::motion=mog motion_denoiser::size=9 motion_detector::sample-frequency=$FREQ ! perf print-cpu-load=true ! fakesink sync=true
| Sample Frequency | Framerate | CPU | GPU |
|---|---|---|---|
| 1 | 29.927 | 10.423 | 48.391 |
| 2 | 30.561 | 8.038 | 41.9 |
| 4 | 30.572 | 7.038 | 22.778 |
