Keywords: Image Enhancement for Night-Vision (IENV), License Plate Recognition (LPR), License Plate Country Identification (LPCI), Vehicle Color Recognition (VCR), Vehicle Make Model Recognition (VMMR), Vehicle Body Style Recognition (VBSR), Vehicle Direction Tracking (VDT), Vehicle Speed Estimation (VSE).

Intro

Have you ever seen a deep learning based ANPR / ALPR (Automatic Number/License Plate Recognition) engine running at 47fps on ARM device (Android, Snapdragon 855, 720p video resolution)?

With an average frame rate as high as 47 fps on ARM devices (Snapdragon 855) this is the fastest ANPR/ALPR implementation you’ll find on the market. Being fast is important but being accurate is crucial. We use state of the art deep learning techniques to offer unmatched accuracy and precision. As a comparison this is #33 times faster than OpenALPR on Android. (see benchmark section for more information).

No need for special or dedicated GPUs, everything is running on CPU with SIMD ARM NEON optimizations, fixed-point math operations and multithreading. This opens the doors for the possibilities of running fully featured ITS (Intelligent Transportation System) solutions on a camera without soliciting a cloud. Being able to run all ITS applications on the device will significantly lower the cost to acquire, deploy and maintain such systems. Please check Device-based versus Cloud-based solution section for more information about how this would reduce the cost.

The next video[A] shows the SDK running on Windows:



The next video[B] shows Image Enhancement for Night-Vision (IENV) result on video stream captured at night:



The code is accelerated on CPU, GPU, VPU and FPGA, thanks to CUDA and OpenVINO.

In addition to Image Enhancement for Night-Vision (IENV), License Plate Recognition (LPR) we support License Plate Country Identification (LPCI), Vehicle Color Recognition (VCR), Vehicle Make Model Recognition (VMMR), Vehicle Body Style Recognition (VBSR), Vehicle Direction Tracking (VDT) and Vehicle Speed Estimation (VSE).

On high-end NVIDIA GPUs like the Tesla V100 the frame rate is 315 fps which means 3.17 millisecond inference time. On high-end CPUs like Intel Xeon the maximum frame rate could be up to 237fps, thanks to OpenVINO. On low-end CPUs like the Raspberry Pi 4 the average frame rate is 12fps.

Don’t take our word for it, come check our implementation. No registration, license key or internet connection is required, just clone the code from Github and start coding/testing. Everything runs on the device, no data is leaving your computer. The code released on Github comes with many ready-to-use samples to help you get started easily.

You can also check our online cloud-based implementation (no registration required) to check out the accuracy and precision before starting to play with the SDK.

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