Passenger wagon CIS number recognition SDK

What is Intlab Coach for?

24/7 real-time optical recognition of CIS numbers of passenger and postal wagons, with a single consolidated recognition result for each wagon.

Intlab Coach is a software development kit (SDK) for third-party integration of optical detection and recognition of eight-digit two-lined passenger and postal wagon numbers (used in the CIS and other countries with 1520 mm track guage). Engine provides the ability to read license plate numbers 24/7 in a broad range of external conditions in individual images as well as video steams. The engine provides the best quality recognition when used with a video stream, since the results received from individual frames taken from different cameras are analyzed and combined using complex algorithms to form a single result for each car. Since the wagon number appears on both sides, the best recognition results are obtained using 2 cameras.

Basic specifications

up to

97%

Recognition accuracy

5-40

Single-frame recognition time

km/h

Maximum rolling stock speed

Minimum character height

Results from individual frames are merged and a single result is issued for each wagon

Supported wagon types

Compatibility

Windows 7, 10

Windows Server

Linux

C/C++

C# (.Net)

Many others

Specifications, system requirements, and the API

Specifications
System requirements
API

Typical probability of accurate recognition results when reading video streams on two sides of wagon	up to 97%
Supported character height	characters as small as 10 pixels are supported, though a height of at least 16 pixels is recommended for best accuracy
Rolling stock speed	up to 80 km/h
Supported wagon types	passenger and postal railcars (and any other wagons with two-line numbers compliant with the Ministry of Railways standard of Russia)
Places where numbers can be read	side of wagons
Number of cameras	1 — 2, recommended 2
Width of camera's monitored area	5 — 10 meters
Camera's angle from the horizontal	<= 20°
Camera's angle from the vertical	<= 30°
Camera's roll angle	<= 5°
Camera's installed height	3 — 3.5 meters
Camera's distance to wagon	1.5 — 10 meters (depending on the focal length of the camera lens); the optimal distance is 4-6 meters
Minimum light level	depends on the video camera used, 50 lux is typical
Supported video signals	individual images or live streams from an analog or digital camera
Average time spent processing each frame with the recommended resolution	no more than 40 milliseconds
Correction of perspective and radial lens distortion	+
Syntactic control and verification of wagon numbers using the Russian Railways checksum	+
Determination of the wagon's direction of motion from the video	+
Consolidated recognition results based on the series of video frames captured as the wagon moves through the monitored area	+
Licensing system	1 license for each instance of a primary / secondary recognition object, USB dongle
Supported programming languages	SDK can be used in applications in C / C ++, C #, VB.Net, Java and any other programming languages that support calling C functions.
Package contents	SDK distribution package, documentation, examples in C / C++, C#, USB license dongle

Supported operating systems

Windows 7,8,10 (32/64 bit), Windows Server 2008, 2012 (32/64 bit), Linux Ubuntu (64 bit)

Recommended computer configuration

Core i3 (4th generation desktop CPU or higher) to perform recognition simultaneously on 1-2 video streams of rolling stock moving up to 10 km/h.
Core i5 (4th generation desktop CPU or higher) to perform recognition simultaneously on 2-4 video streams of rolling stock moving up to 10 km/h.
Core i7 (4th generation desktop CPU or higher, 4 cores) to perform recognition simultaneously on 1-4 video streams of rolling stock for speeds above 10 km/h.
Core i7 (4th generation desktop CPU or higher, 8 cores) to perform recognition simultaneously on 5-8 video streams of rolling stock for speeds above 10 km/h.
RAM: 4 Gb or greater.

Engine input	images loaded from a file or passed in a memory buffer in BMP, JPEG, or RAW format real-time video stream passed in a memory buffer in BMP, JPEG, or RAW format, event indicating when wagon first appears in the monitored area, event indicating when the wagon has left the monitored area
Engine settings	Frame resolution, rectangular recognition zone within the frame (region of interest - ROI), minimum and maximum size of characters in numbers, average character height, average aspect ratio, parameters for camera tilt correction (optional), parameters for radial distortion correction (optional)
Engine output	When the engine receives an event indicating that the wagon has left the frame or stopped, the following results are returned: the set of the best recognition hypotheses for each separate frame, where each hypothesis contains a string representation of the number, a recognition confidence score (hypothesis weight), the timestamp and image of the frame with the highest confidence score for this hypothesis, the location of the number within the frame, and the timestamps of the first and last frames where the wagon number was detected; the final result, obtained by combining the results from each frame, contains a string representation of the number with the recognition confidence score (hypothesis weight), recognition confidence score for each character, direction of wagon motion, and a link to the recognition data from the best camera;

Examples of successfully recognized CIS coach numbers

Key advantages

Speed

The recognition engine works smoothly on a video stream (up to 50 fps) of rolling stock moving even at high speed, performing recognition using up to 4 cameras on a single railway track, while also maintaining the highest possible recognition quality.

High recognition accuracy

Our superior recognition accuracy, which is confirmed by internal and third-party testing, is achieved thanks to the high recognition speed (which ensures that frames are not skipped), most advanced recognition algorithms, smart unification of per-frame results, and the ability to use up to 4 cameras from both sides of a wagon to recognize numbers on the wagon sides and chassis.

Reliability

In more than 12 years of actively working on wagon number recognition, we have amassed a wealth of experience and an extensive test set (more than 50,000 wagons) containing every variety of wagon from a large number of OAO Russian Railways sites and large business enterprises. This gives us confidence that our product will work very well in any, even unfavorable, circumstances. When installed as recommended and correctly configured, it has the best performance on the market. Since 2009, our clients' software and hardware solutions have represented a substantial share of the optical wagon number recognition systems delivered to OAO Russian Railways sites.

Fast and simple development

We strive to provide the most flexible, functional, convenient, and coherent APIs for all of our product lineups. We provide developer support and consultations during integration of our products into client solutions. We care about our clients and strive as much as possible to maintain backwards compatibility and support older versions of the API.

Inhouse development

Our engineers developed all of our recognition libraries from scratch, putting us in the best position to answer customers' questions, since there are no third-party dependencies.

Hardware independence

Our video analytics engines are not tied to any specialized equipment or cameras. You can use any hardware that meets the technical requirements.

Friendly and effective support

A qualified expert developer will quickly answer even your most difficult question.

Personal approach / Custom modifications

We are always open to collaboration and take customers' wishes into consideration in the development of future versions. Our flexible, modular recognition engine also makes it possible to rapidly solve non-standard tasks (number recognition on a company's rolling stock with a proprietary numbering system, etc.)

Superb qualifications

12+ years of real-world experience conducting research, and developing and perfecting software in the area of OCR and computer vision.

Continuous improvement

During the lineup of product's more than 12 years of existence, we have worked continuously to improve the technical characteristics of the recognition engine. Having a lineup of number recognition products lets us continuously grow our expertise while simultaneously advancing and improving the recognition kernel in the entire product lineup.