Saturday, January 13, 2018

ANPR / NPR / LPR technology

ANPR / NPR / LPR technology

Automatic Number Plate Recognition (ANPR) is the next growth product to take off in the world  in fact, it is already beginning to be the biggest potential earner for installation companies. One problem is that it crosses technological barriers and the needs of available bandwidth were conveniently obscured.

On the other hand ANPR technology is completely within the scope of knowledge of CCTV companies, although there are a few new terms and technologies to come to grips with. We will start by considering ANPR for a single lane using a dedicated camera and go on to discuss some other applications such as multi-lane systems and Town Centre systems with colour cameras.

Core Technology
If you scan a document into your PC and then open it in a word processor you cannot edit or alter it in any way. This is because it is simply one bitmap made up of thousands of individual pixels. However there is software available, frequently a freebie with scanners that can convert these groups of pixels into characters. This is Optical Character Recognition (OCR), which scans each group of pixels and estimates whether or not it could be a letter and replaces the pixels with the ASCII* code for the letter. For instance the ASCII code for the lower case 'a' is 01100001. So, the software scans the whole document and produces a page of letters exactly the same as though you had typed them in, which can be edited or manipulated in any way.

(*) American Standard Code for Information Interchange.

OCR is the fundamental technology used in ANPR and provides the capability to store and sort data. ANPR cameras need to be a special type and set up within certain important parameters as will be described later.

As a vehicle approaches the camera the software takes a series of 'snapshots' and stores them in a file. When the number plate is of sufficient size for the OCR software the frame is scanned and the registration number is converted to ASCII code and held in a list. This continues for a series of images according to the speed and position of the vehicle. The list is scanned for similarities and a 'favourite' selected to retain. The system would typically scan and compare 10-15 images, with 5 being considered the minimum for high accuracy. Note that this is the principle of the software we are describing; some systems only take one image at a certain position.

This then, is the start of the ANPR capture and is totally dependant on the correct set up of camera, lens, illumination, angle of view and configuration. Get one wrong and you have a disappointed customer who won't pay the bill.

At this stage we are concentrating on the number plate capture but there are many other aspects to be considered for a completely integrated system, which will be discussed later. Note that the ANPR capture considered here is monochrome.


The Behaviour of Reflected Light 
To explain some further technology behind successful ANPR capture we need to look at the behaviour of light. A basic law of light is that the angle of incidence equals the angle of reflection. However, number plates in the UK and most other countries have a special characteristic; they are known as RETRO-REFLECTIVE. The surface is covered in hundreds of tiny hemispheres which cause light to be reflected back to the source. This is the same technology used in safety clothing and signs. No matter from which direction the light is directed, it always reflects back and makes them very visible. 

The Application of Infrared Illumination to ANPR
If a standard colour or monochrome camera was focused to read number plates it would have to contend with a huge variety of lighting conditions, daytime, night-time, sunlight, backlight, headlights, and so on. One configuration simply would not cope with all conditions, so there is a need to provide a constant level and direction of illumination irrespective of any other conditions. And so we come to the development of special cameras for continuous capture of number plate data.
The camera must be sensitive to the infrared part of the spectrum, to at least 850 nanometres. Then it must be fitted with a filter to restrict the visible part of the spectrum. The lens would have a manual iris set fully open and the shutter speed set to 1/1000th second. Finally an infrared source must be fitted adjacent to the camera.

Therefore, taking advantage of the retro-reflective characteristics of number plates, the illumination from the illuminator will be reflected directly back to the camera. Thus only infrared light will be seen without any visible light or other reflections or refractions. The picture will of course be black with no detail except for the number plate. The OCR software then takes care of converting the image to usable code.
Note that this is the sort of image on the monitor both day and night.

Cameras and Lenses
This then is the core of ANPR technology, but there are many other factors to be considered. The first of these are the selection of lens and the distance to view the vehicle. The size of a UK license plate on cars and commercial vehicles is approximately 510mm long x 110mm high. Motorcycles are different being approximately 255 x 200. However more significantly, the minimum height of the letters must be 79mm. The current UK font is Charles Wright, although there are some illegal formats seen. The size of the number plate and the actual characters will need to be of a certain size when seen by the camera for the OCR software to function. One line of thought is that the number plate should be 18% of the scene width; I prefer to consider the vertical height of the characters, which from previous research should be 3% minimum for a 400 line camera. This in fact equates very closely to the 18% screen width but is more logical when considering different shapes of number plates. (For instance when a car plate is 18% of the screen a motorcycle plate would only be 7 %.) Also note that motorcycles currently do not have to carry a front number plate, but this could change in the future.

This provides the first convenient way to calculate the lens angle. For 79mm high characters the scene height needs to be 2633mm. (79 being 3% of 2633). Therefore using a scene height of 2.633M and the known distance, it is a simple matter to calculate the lens angle and thus the focal length. At this stage the height of the camera has not been considered but would not make much difference for normal combinations of distance and camera heights. (See further notes at the end).The following table shows the lens angle for various distances and a scene height of 2.633M.

Distance to target
5M
10
15
20
25
30
35
40
45
50
Lens angle
29.5°
15°
10°
7.5°
4.3°
3.8°
3.4°
Focal length 2/3"
13mm
25
38
50
63
76
88
99
111
126
Focal length ½"
9mm
18
27
37
46
55
64
72
81
92
Focal length 1/3"
7mm
14
21
27
34
41
48
54
61
69
Focal length ¼"
5mm
10
15
20
25
30
35
40
45
51

From this table you can find the nearest lens focal length for the sensor size. Where the focal length falls between two available lenses, a vari-focal lens can solve the issue.
In instances where the camera height is large compared to the horizontal distance the number plate can produce the effect of being rotated vertically. It is important to check with the software provider if this is acceptable.

Vehicle Speed and Capture Rate
Another very important consideration is the speed of the vehicles to be monitored and the width of the area to be covered. There are again two important interrelated elements connected with vehicle speed. One is the rate of the image grabber software the other is the speed of the processor. With modern processors the latter can no be ignored.
Consider a vehicle travelling at 30 MPH, this equates to 13.4 Metres in one second. A UK camera produces 50 fields per second and the shutter speed is usually 1/50th second. In 1/50th second the vehicle would travel 0.27 Metres (268mm). This would cause a blurred image and problems with the OCR translation. For this reason the shutter speed should be set to 1/1000th second, in which case the vehicle would travel only 0.013 Metres (13.4mm). The same reasoning applies when trying to capture moving images with a 35mm camera. This is slightly less important when looking head on at a vehicle but becomes increasingly important when the vehicle is at an angle approaching the camera.

Camera Positioning
Where the camera is positioned other than directly in the line of the approaching vehicle the ANPR provider must be consulted. Many systems will not function with more than more 1 or 2 degrees of horizontal skew or vertical rotation.
The positioning of the camera is a most important consideration for satisfactory operation of an ANPR system. This can vary the percentage of recognitions to number of vehicles from 30% or 40% to near on 100%. The camera location depends on several factors, such as: 

Single camera covering a barrier entrance
Probably the best position is for a camera and illuminator in a 1M high bollard viewing directly at the approaching vehicle.

Single camera covering one lane
This could be a pole mounted unit about from 18M to 30M from the vehicle.

Single or multiple cameras covering multiple lanes
This is a special application requiring input from the ANPR provider.

The first thing to address is the shutter speed if it is adjustable. The best would be if the speed can be set remotely, if not each camera needs to be visited and the speed set manually. The optimum setting is to 1/1000th. Alternative settings may be 1/250th for traffic up to 5 MPH and 1/500th for traffic up to 40 MPH. Note that all these settings will affect the low-light capability of the cameras and a compromise may be required.
Another consideration is that the camera positions and heights would not be at the optimum for ANPR. Particular attention must be paid to the angles of skew and rotation and a guaranty obtained that an acceptable percentage of recognitions will be achieved.

Cameras on motorway bridges
Again a special application requiring input from the provider.

Congestion charging cameras
This application requires input from the ANPR provider and local authority before even starting to think of a specification.

Cameras in Police vehicles
These are normally colour cameras mounted on a swivel mount and can view images to the front or either side of the vehicle. This is another special application requiring input from the provider.

Overview cameras
It is often necessary to have a conventional colour image of the vehicle especially where prosecution or congestion charging is the application. This would be a separate colour camera mounted alongside or just below the ANPR camera. Saving the overview image is triggered by the ANPR camera registering a number plate. This then adds a colour image to the same file for future reference. It is generally a false economy to attempt to combine the number plate recognition and overview using a single camera for 24/7 operation.

Multi-national number plates
There are thousands overseas vehicles on the roads today, many of them with symbols and other labels incorporated into the plate. The ANPR system must be capable of reading all of these. The software should have a built-in list of such plate styles.

The ANPR database
Just capturing number plates and storing them is not much use by itself. The screenshot below shows an ANPR example review screen. The associated database should be able to provide much more information. 
Searching should possible on several fields:
  • Full plate.
  • Part plate.
  • Time.
  • Date.
  • Category.
  • Notes added to image file.
Further functions could be:
  • Counting vehicles in and out of premises, leaving a list of all vehicles on site.
  • The length of time a vehicle is on site.
  • Vehicle speed (from two cameras).
  • Employee names can be associated with number plates and access allowed or denied to certain areas.
 Example flow diagram for ANPR system 

Vehicle speeds and distances
The following table shows distances travelled for different speeds. This will give some indication to the number of images that be captured. From this table it can be seen that fof a vehicle travelling at 50MPH at 1/50th exposure it will travel nearly half a metre resulting in a blurred image. Compared to only 22mm at 1/1000th exposure.

Image grabbers
These are sometimes known as frame grabbers or field grabbers or stores. In reality they all store images or single fields.
This is a particular area where you really pay for what you get. The main criterion is the speed at which images can be captured. For instance Video for Windows can only store about 8 images per second which is only suitable for very slow moving or stationary traffic.

ANPR is the generic term generally used in the UK but other terms are common in other parts of the world. Such as; NPR (Number Plate Recognition), LPR (License Plate Recognition). To know more please click on License Plate Recognition 21st

The key elements that go into the solution are a competent IP camera and an analytic software that converts images to computer data. It is important that the camera is capable of capturing excellent quality images, as the analytics software is heavily dependent on this.

Genetec AutoVu SharpX ALPR camera
SharpX is an IP-based automatic license plate recognition (ALPR) camera designed for demanding mobile and fixed applications. With its small form factor, high resolution and integrated illumination, the SharpX captures more license plates in a variety of conditions and at high speeds. Supporting up to four cameras connected to a single external processing unit.

IntelliVision License Plate Recognizer
IntelliVision’s License Plate Recognizer is a deep learning and AI-based license plate detection, recognition and search solution with an accuracy as high as 98%. It takes into account variables such as movement and high speeds which are natural to the environment of vehicle monitoring. Real-time searching can also be performed on each plate detected, comparing the information gathered with a stored database of license plates.

VIVOTEK IP816A-LPC-v2 Kit
IP816A-LPC-v2 Kit represents a total solution for license plate capture (LPC) applications by leveraging the top-tier image quality and traffic monitoring ability of the IP816A-LPC box camera. The CaMate IR illuminator utilizing CVA Tech provides adjustable beam angles for different distances. A headlight filter is implemented to reduce instances of glare from direct headlight contact. The kit delivers clear license plate images for reliable recognition, in both the sunniest days and the darkest nights. Empowered with VIVOTEK Scene Mode, which provides different exposure levels to avoid overexposure in some lighting conditions, the kit can capture clear plate images from the vehicles moving at speeds of up to 85 mph (140 km/hr).

PlateSmart ARES Viewer
ARES Viewer is a stand-alone application with an easy-to-use interface that allows widespread and quick access to vehicle recognition data and alerts in real time. It communicates vital information and allows users to view real-time data from anywhere, at any time, through a VPN connection to their network. ARES Viewer safely locks away the administrative power of the command center and makes plate and notification information available for each licensed user.

Luxriot license plate recognition software

Luxriot LPR is an automatic license plate recognition application that is designed to work with the Luxriot VMS Server. This application accommodates customers with the specific need to detect, recognize and register motor vehicle license plates, and then stores recognized license plates and snapshots on local or central database. Luxriot LPR, which supports a multitude of IP and analog cameras, is ideal for vehicle access control applications as well as for traffic control and enforcement applications.

কলকাতার বেশ কিছু জায়গায় বসেছে অটোম্যাটিক নম্বর প্লেট রিকগনিশন (.এন.পি.আর.) ক্যামেরা।এই ক্যামেরা এমন বি়জ্ঞানসম্মত ভাবে বসানো হয়েছে যে নম্বর ছাড়াও গাড়িতে কারা বসে রয়েছেন, তাঁদের চেহারা এবং গতিবিধিও পরিষ্কার ধরা পড়ছে। অপরাধ দমনে যাহা গুরুত্বপূর্ণ ভূমিকা পালন করে চলেছে | জেনে নেওয়া যাক কি এই .এন.পি.আর. ক্যামেরা (ANPR / LPR Camera ) ?

ক্যামেরাটির বিশেষত্ব হল, এখানে যে কোনও গাড়ির নম্বর স্বয়ংক্রিয় ভাবে ধরা পড়বে। এমনকি ঘণ্টায় ৫০-৬০ কিলোমিটার গতিবেগে চলা কোনও গাড়ির নম্বর প্লেটের ছবিও স্পষ্ট ভাবে নিতে পারে উচ্চ ক্ষমতাসম্পন্ন এএনপিআর ক্যামেরা। এই ক্যামেরা মূলত অপটিক্যাল ক্যারেক্টার রিকগনিশন প্রযুক্তিতে কাজ করে। অপ্টিকাল ক্যারেক্টার রেকগনিশন হাতে লেখা বা টাইপরাইটারে টাইপকৃত লেখার ছবিকে (সাধারণত স্ক্যানার দিয়ে স্ক্যান করা) কম্পিউটারে সম্পাদনাযোগ্য লেখায় অনূদিত করার যান্ত্রিক বা ইলেকট্রনিক পদ্ধতিকে বুঝায়। ক্যামেরা টি একটি উচ্চ ক্ষমতা সম্পন্ন সার্ভার সফটওয়্যার এর সাথে যুক্ত করা হয়, যাহা একটি ডাটাবেস তৈরী করে, ওই ডাটাবেস গাড়ির নম্বর সহ স্থান-কাল-পাত্র জমা হয় প্রয়োজন মতো সেই ডাটাবেস থেকে প্রয়োজনীয় তথ্য বের করে নেওয়া যেতে পারে।

গত ০৮ সেপ্টেম্বর, ২০১৭ তে ১২ বছরের মেয়ের ব্রেন টিউমার অপারেশনের জন্য ভারতে এসেছিলেন  বাংলাদেশের নাগরিক মহম্মদ নুরউদ্দিন আহমেদ। সপরিবারে বিমানবন্দর থেকে ট্যাক্সিতে করে হাওড়া যান তিনি। ট্রেন ধরে চেন্নাই যাওয়ার কথা ছিল। কিন্তু হঠাৎই খেয়াল পড়ে, একটি ব্যাগ ফেলে এসেছেন ট্যাক্সিতে। ওই ব্যাগেই ছিল টাকাপয়সা, জরুরি কাগজপত্র। প্রথমে হাওড়া স্টেশন চত্বরের সিসিটিভি ফুটেজ খতিয়ে দেখে পুলিশ। অটোমেটিক নম্বর প্লেট রিকগনিশন বা ANPR ক্যামেরার মাধ্যমে উদ্ধার হয় ট্যাক্সির নম্বর। খোঁজ মেলে ট্যাক্সি মালিকের। উদ্ধার হয় ব্যাগ।

তাছাড়া এই ক্যামেরা উচ্চ আলোক এর মধ্যেও বস্তু / চেহেরা চিনতে সখ্যম। ক্যামেরা তে থাকে এইচএলসি (হাই লাইট কমপেনসেশন) প্রযুক্তি, ঠিক যেখানে আলোক উৎস সেখানে ব্রাইটনেস পরিবর্তনের মাধ্যমে একটি কালো বিম্ব তৈরী করে নিজের পরিবর্তনশীল লেন্স এবং ইনফ্রারেড সেনসর মাধ্যমে পূর্ণ ছবিকে দৃশ্যমান করে, যাহা আমাদের চোখ হয়তো পারেনা।

উচ্চ প্রযুক্তির ক্যামেরা বসানো বড়ো কথা নয়, বড়ো কথা হলো
. পরিমিত রক্ষণাবেক্ষণ, দেখা গেছে রক্ষণাবেক্ষণ এর অভাবে অনেক দামি ইলেকট্রনিক দ্রব্য নষ্ট হয়ে যায়।
. সঠিক ভাবে নিরীক্ষণ - সফটওয়্যার ঠিকমতো তথ্য সংরক্ষণ হচ্ছেকিনা এবং ক্যামেরা সঠিক দিকে  তাকিয়ে আছেকিনা, সেটা প্রতিনিয়ত দেখা উচিত তাছাড়া নিয়ম করে সংরক্ষিত তথ্য নিয়মিত ভাবে খুঁটিয়ে দেখা অত্যন্ত জরুরি।

. প্রচার - একটি অন্ত্যন্ত গুরুত্বপূর্ণ অধ্যায়, সকলের জ্ঞাত হওয়া উচিত এই পরিসরে ক্যামেরা বর্তমান, রাস্তায় সুরখ্যা লাইন পারকরলেই ক্যামেরা তে ধরা পড়বে, তাহার জন্য মোটা অংকের জরিমানা হতে পারে.
Add caption

Saturday, January 6, 2018

DRI in Video Surveillance

DRI in Video Surveillance

Wish you a very happy new year 2018. In today’s competitive environment, having new customers constantly is one of the keys of success. To make professional quotes with proper sketches, realistic test objects, 3D images, mock ups etc, which leave a lasting impression on your clients.
When you are answering queries related to your product, you can quickly glance through the details while your client is on call.
DRI stands for Detection, Recognition and Identification in video surveillance. DRI ranges, expressed in meters, km (or miles), can be found in the specification table of infrared camera brochures. In order to select the right sensor meeting the application requirements, these DRI ranges have to be, first, perfectly defined, but also assessed with regards to globally adopted industrial standards.

DRI Definition

The terms “Detection”, “Recognition” and “Identification” were defined as follow:
  • Detection: ability to distinguish an object from the background
  • Recognition: ability to classify the object class (animal, human, vehicle, boat …)
  • Identification: ability to describe the object in details (a man with a hat, a deer, a Jeep …)

The following pictures illustrate these definitions:
    Left image: Detection – At several kms, 2 targets are detected out of the background 
    Center image: Recognition - a human is walking along the fence 
    Right image: Identification – 2 males with trousers and jackets are identified – one is smoking.
We detect an object when it enters the field of view. Detection means we are aware that an object (or person) now exists where previously it was not seen. Usually, this is due to movement of the object into the field of view of the surveillance camera. Detection simply means we are aware of the object, but have too little details to recognize or identify the object.
As the object moves closer, we may recognize the object from characteristics previously encountered. For example, aircraft recognition is taught to military ground troops and airmen. All aircraft have wings, engines, a fuselage, and tail assembly. They differ in size, shape, number, and position to each other. A particular model of aircraft can be recognized by recalling these characteristics from pictures, drawings or past detailed observations.
Identification is the process where sufficient details are available to uniquely discern a person or object that is previously unknown. Identification requires sufficient detail to accurately describe or recall the characteristics of the subject at a later time. For example, a mug shot (booking photograph) is taken following the arrest of a subject as a means of photographing (recording) sufficient details for later identification by a victim or witness. In video surveillance terms, sufficient detail is calibrated in pixels per foot of the area recorded by the camera.

DRI Ranges

“A picture is worth a thousand words” goes the old and equally wise saying. It is true that most human beings learn much quicker when aided by visuals rather than pure text as that is more in tune with the human psychology of learning.
So instead of remembering commands and functions you will easily remember that yellow color indicates areas where it is possible to recognize people, red color indicates areas suitable for people identification, green areas for detection and so forth.
To put this in perspective, sensors have a resolution of 640×480 which is over 300,000 pixels.
Human “detection” only requires 3.6 of those pixels and “identification” only requires 230 pixels, which is an extraordinarily small amount on the screen that can easily go unnoticed by the human eye. In fact, if this page were the size of your video feed, the area required for a human detection rating is about equivalent to a lowercase letter “i” in this text. The amount of detail visible at the detection, recognition and identification distances is not as high as one might expect, as can be seen in the chart below.
Another thing that is often not mentioned is that these ratings are based on what is termed “ideal conditions” which rarely happen in the real world. The average environmental application will get 25% less than the distance that the thermal camera is rated for and in extreme conditions can be less than 10% of the rated distance.

Based on SR-100 & SR-100P by FLIR the approximate DRI (Detection, Recognition and Identification) range for a vehicle and a human target is listed in Tables 1 & 2.
Lens
Detection
Recognition
Identification
100mm
4.4km
1.1km
580m
Table 1: DRI range for Vehicle with 2.3m critical dimension using a Thermal Security Camera with 38 micron pitch detector

Lens
Detection
Recognition
Identification
100mm
1.6km
400m
200m
Table 2: DRI range for Human Target using a Thermal Security Camera with 38 micron pitch detector
Assumptions:  50% probability of achieving objective at the specified distance given a   2-degree temperature difference and 0.85/km atmospheric attenuation factor.  Actual range may vary depending on camera setup, user experience, environmental conditions, and type of monitor or display used.
Each & every Camera has this DRI ranges, before preparing project oriented ppt, do confirm the ranges & show in a slide with sketch.
Artical publish at safe secure magazine January 2018 issue.

Warrior 2.0 26BF2 is a 1080P 2MP Fixed Lens Mini Bullet Camera
Detect: 100 ft
Recognize: 50 ft
Identify: 25 ft

Laser 2.0 - 26ZV-LIR is a 2MP (1080P) IP PTZ Camera with 33x Optical Zoom & Laser Infrared camera and Spotlight 2.0 - 26ZV-W is a 2MP (1080P) IP PTZ Camera with 22x Optical Zoom & Full Spectrum Light Camera
Detect: 950 ft
Recognize: 875 ft
Identify: 750 ft

Lookout 2.0 - 26ZV is a 2MP (1080P) IP PTZ Camera with 30x Optical Zoom Camera
Detect: 1000 ft
Recognize: 900 ft
Identify: 800 ft

Judge 8.0 - 26DV8 is a 4K (8MP - 4x1080P) Multi-Purpose Lens Dome Camera with Motorized Zoom and IK10 Camera and Gladiator 8.0 - 26BV8 is a 4K (8MP - 4x1080P) Multi-Purpose Lens Bullet Camera with Motorized Zoom and P-Iris Camera
Detect: up to 200 ft
Recognize: up to 150 ft
Identify: up to 100 ft

Deputy 4.0 - 26DF4 - 4MP (2x1080P) Fixed Wide Angle Lens Turret Dome Camera
Detect: 115 ft
Recognize: 50 ft
Identify: 35 ft

Archer 2.0 - 26BV2-L - 2MP Long Range, Low Light Bullet Camera with Motorized Zoom and Focus
Detect: up to 450 ft
Recognize: up to 350 ft
Identify: up to 300 ft

Warrior 4.0 - 26BF4 is a 4MP (2x1080P) Fixed Lens Mini Bullet Camera
Detect: 150 ft
Recognize: 75 ft
Identify: 50 ft