Benchmarking

Drive Rekor Scout® on all CPU cores to benchmark speed for various video resolutions.

Example Benchmarks

Benchmarks demonstrating the power of Rekor Scout are presented below. If you find yourself thinking, “These numbers look too good to be true”, we understand. Don't take our word for it.

‍Try it out yourself!

Plate number accuracy

Benchmark
License plate state identified
Correct in 1 of 10 estimates
Correct on 1st estimate

OpenALPR Open Source

0.00%

65.69%

42.16%

Rekor Scout®

96.83%

100.00%

99.02%

CPU hardware video performance

Benchmarks were conducted on Ubuntu Linux using Intel systems and integrated graphics.

System profile
1080p
720p
480p

Xeon E5-2666 v3 @ 2.9 GHz

12.7 FPS

15.0 FPS

16.8 FPS

Core i5-5250U @ 1.6 GHz

17.4 FPS

20.4 FPS

22.9 FPS

Xeon E7-8880 v3 @ 2.3 GHz

20.1 FPS

23.4 FPS

26.2 FPS

Core i7-7700K @ 4.2 GHz

52.4 FPS

61.8 FPS

69.9 FPS

Core i7-8750H @ 2.2 GHz

53.3 FPS

60.8 FPS

67.3 FPS

Xeon Platinum 8124M @ 3.0 GHz

203.8 FPS

236.0 FPS

275.5 FPS

NVIDIA GPU video performance

Benchmarks were conducted on Ubuntu Linux using varied systems with NVIDIA GPU Acceleration enabled.

System profile
1080p
720p
480p

NVIDIA Jetson TX-1

6 FPS

15 FPS

44 FPS

NVIDIA Jetson TX-2

14 FPS

34 FPS

86 FPS

NVIDIA Tesla M60

99 FPS

164 FPS

202 FPS

NVIDIA GeForce GTX 1060

165 FPS

191 FPS

206 FPS

NVIDIA Tesla V100

184 FPS

329 FPS

364 FPS

NVIDIA GeForce RTX 2080

221 FPS

297 FPS

351 FPS

Prerequisites

  • Rekor Scout® Basic or Pro license/subscription

  • Ubuntu 22.04, Ubuntu 20.04, Ubuntu 18.04, Windows 10, or Windows 11

  • Python (2 or 3)

Installation

  1. Clone this repository git clone https://github.com/openalpr/speed_benchmark.git

  2. Install the Python requirements pip install -r requirements.txt

Usage

  1. View all command line options by running python speed_benchmark.py -h

  2. Select your desired resolution(s) - vga, 720p, 1080p, and/or 4k

  3. Benchmark using the default flags (1 stream and no minimum CPU threshold) by running python speed_benchmark.py

  4. Check the average CPU utilization (see sample output below). Resolutions with utilization of less than 95% are bottlenecked on decoding the video stream (typical for higher resolutions). These should be rerun with additional streams for a better estimate of maximum performance

  5. Set the --thres to a non-zero value. This causes the program to add streams until the threshold CPU utilization is achieved. We recommend using 90 < thres < 95. On large systems where the CPU utilization for a single stream is much lower than your desired threshold, you can reduce the granularity of the search by setting --steps > 1

  6. Estimate the number of cameras for a given total FPS value by using the following per-camera rules of thumb

    • Low Speed (under 25 mph): 5-10 fps

    • Medium Speed (25-45 mph): 10-15 fps

    • High Speed (over 45 mph): 15-30 fps

Sample Output

Using default options

user@ubuntu:~/git/speed-bench$ python speed_benchmark.py
Initializing...
	Operating system: Linux
	CPU model: Intel Core i7-8750H CPU @ 2.20GHz
	OpenALPR version: 2.7.101
	Runtime data: /usr/share/openalpr/runtime_data
	OpenALPR configuration: /usr/share/openalpr/config/openalpr.defaults.conf
Downloading benchmark videos...
	Downloaded vga
	Downloaded 720p
	Downloaded 1080p
	Downloaded 4k
Testing with 1 stream(s)...
	Processing vga
	Processing 720p
	Processing 1080p
	Processing 4k
	Lowest average CPU usage 81.4%
+---------------------------------------------------------+
|        OpenALPR Speed: 1 stream(s) on 12 threads        |
+------------+-----------+-----------+-----------+--------+
| Resolution | Total FPS | CPU (Avg) | CPU (Max) | Frames |
+------------+-----------+-----------+-----------+--------+
|    vga     |    52.9   |    81.4   |    99.4   |  479   |
|    720p    |    49.6   |    84.9   |    99.5   |  479   |
|   1080p    |    44.4   |    88.8   |   100.0   |  479   |
|     4k     |    23.8   |    93.7   |   100.0   |  479   |
+------------+-----------+-----------+-----------+--------+
Saving results to /home/user/git/speed_benchmark/speed-bench-20190618.csv

Starting with 3 streams and incrementing by 2 each time 95% CPU utilization is not achieved

user@ubuntu:~/git/speed-bench$ python speed_benchmark.py --thres 95 --streams 3 --step 2
Initializing...
	Operating system: Linux
	CPU model: Intel Core i7-8750H CPU @ 2.20GHz
	OpenALPR version: 2.7.101
	Runtime data: /usr/share/openalpr/runtime_data
	OpenALPR configuration: /usr/share/openalpr/config/openalpr.defaults.conf
Downloading benchmark videos...
	Found local vga
	Found local 720p
	Found local 1080p
	Found local 4k
Testing with 3 stream(s)...
	Processing vga
	Processing 720p
	Processing 1080p
	Processing 4k
	Lowest average CPU usage 93.2%
Testing with 5 stream(s)...
	Processing vga
	Processing 720p
	Processing 1080p
	Processing 4k
	Lowest average CPU usage 95.3%
+---------------------------------------------------------+
|        OpenALPR Speed: 5 stream(s) on 12 threads        |
+------------+-----------+-----------+-----------+--------+
| Resolution | Total FPS | CPU (Avg) | CPU (Max) | Frames |
+------------+-----------+-----------+-----------+--------+
|    vga     |    66.5   |    95.3   |   100.0   |  479   |
|    720p    |    61.3   |    96.2   |   100.0   |  479   |
|   1080p    |    54.1   |    97.3   |   100.0   |  479   |
|     4k     |    29.5   |    99.2   |   100.0   |  479   |
+------------+-----------+-----------+-----------+--------+
Saving results to /home/user/git/speed_benchmark/speed-bench-20190618.csv

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