smart camera
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The smart camera a whole vision system contained in one neat housing can be used anywhere, in any industry where image processing can be applied. Companies no longer need a cabinet in which to keep all their computing equipment: the computer is housed within the smart camera. In the pharmaceutical industry and in clean rooms when not even dust is allowed this can be a big advantage. A single square metre of space can be comparatively very expensive if there is no need for a component rack or cabinet, simply a smart camera, then this could save a lot of money. In particular, there would not be the usual cabling involved for other vision systems, and set-up is simple . Later in this communication are stated some advantages of using smart cameras or PC-based systems in vision applications.
In usual vision systems scenarios, only a small fraction of a picture frame will be the region of interest (ROI). In fact, often no visual image of the ROI is even required. The object of a vision system, after all, is to make a decision: "Is there a blob"? "Where is the blob"? "Is this a defect"?
What if all that pixel pre-processing and decision-making could be done within the cam- era? If all the processing were done inside the camera, the blob analysis of a gigabit image might result in only a few hundred bytes of data which need to be sent somewhere. Such compact packets of data could be easily transmitted directly to a machine control without even passing through a PC.
To illustrate this, a smart cameraâ„¢s embedded system architecture is shown along with an example of a hardware embedded image processing algorithm: Static Gray Scale Thresholding.
Many global companies including Glaxo and Ciba-Geigy, and motor companies such as Ford and Volkswagen are users of smart cameras. Others include Sony Music and 3M;latter has in place up to 150 smart camera systems [1]. A small example of a vision system for web inspection is shown where twenty smart cameras are connected together. Many changes are yet to come concerning smart cameras as in technology as well as in there future applications. An overview, concerning these issues, is made closing this communication. We have to recognise however that smart cameras will not be the answer for all vision applications.
Smart Cameras vs. Standard Smart Vision System
The question often comes up as to what is the most appropriate approach to take in implementing a vision system - using a smart camera or using some sort of PC-based approach. There is no question that as the microprocessors, DSPs and FPGAs are getting faster and, therefore, more capable, smart cameras are getting smarter. Therefore, they are a challenge to more ''traditional'' approaches to vision systems. Significantly, however, ''traditional'' approaches are also taking advantage of the advances and so, too, are faster and smarter.
Traditional approaches usually mean a PC-based implementation. This could be either using a camera with the capability to interface directly to the PC (IEEE 1394/Firewire, Camera Link, LVDS, USB, etc.), or a system based on a frame grabber or other intelligent image processing board or vision engine that plugs into the PC. In this latter case, more conventional analog cameras are used as the input device. A smart camera, on the other hand, is a self-contained unit. It includes the imager as well as the ''intelligence'' and related I/O capabilities. Because this format resembles the format of many intelligent sensors, these products are often referred to as ''vision sensors. ''However, a vision sensor often has a limited and fixed performance envelope, while a smart camera has more flexibility or tools, inherently capable of being programmed to handle many imaging algorithms and application functions. A PC-based vision system is generally recognised as having the greatest flexibility and, therefore, capable of handling a wider range of applications.
PC based vision system:
The PC based system advantages include:
Flexibility - The PC offers greater flexibility in the number of options that can be selected. For example one can use a line scan versus an area scan camera with the PC. One can use third party software packages with the PC approach (smart cameras tend to be single source software).
Power - PC's tend to offer greater power and speed due in large part to the speed of the Intel processors used internally. This power in turn means that PC's are used to handle the ''tougher'' applications in vision systems.
Smart Camera Advantages:
The smart cameras advantages include:
Cost - Smart cameras are generally less expensive to purchase and set up than the PC- based solution, since they include the camera, lenses, lighting (sometimes), cabling and processing.
Simplicity - Software tools available with smart cameras are of the point-and-click variety and are easier to use than those available on PC's. Algorithms come pre-packaged and do not need to be developed, thus making the smart camera quicker to setup and use.
Integration - Given their unified packaging, smart cameras are easier to integrate into the manufacturing environment.
Reliability _ With fewer moving components and lower temperatures, smart cameras are more reliable than PC's.
In general the performance of the smart camera will continue to increase. This will mean that the smart camera will be used for more difficult applications, slowly displacing the PC approach .

The smart camera presented in this communication reduces the amount of data generated to the Ëœdata of interestâ„¢ by making use of embedded image processing algorithms. The data of interest might be, for example, defective areas of the product being inspected. Multiple cameras can route their data to a single frame grabber and computer due to the reduction of data stream, thus dramatically reducing system cost and increasing inspection bandwidth capability. This smart camera also makes use of an on-board microprocessor for communication with the inspection systemsâ„¢ host computer and for internal control functions .
The following block diagram illustrates the camera architecture.

A detailed explanation of the camera architecture follows, starting with the image sensor.

Image Sensor Basics:
In this smart camera, a CCD (Charge Coupled Device) image sensor converts photons (light) into electrons (charge). When photons hit an image sensor, the sensor accumulates electrons. This is called charge integration. The brighter your light source, the more pho- tons available for the sensor to integrate, and the smaller the amount of time required to collect a given amount of light energy. Finally, the sensor transfers its aggregate charge to readout registers, which feed each pixelâ„¢s charge from the image sensor into an output node that converts the charges into voltages. After this transfer and conversion, the voltages are amplified to become the cam- eraâ„¢s analog output.
Analog to Digital Conversion Electronics:
The analog output of the CCD is converted to a digital output for further processing. The camera presented here sub-divides the CCD analog output into eight channels of 256 pixel elements each. Analog to digital conversion is performed at a 20 MHz data rate for each channel thus yielding an effective camera data rate of 160 MHz. The digital data is then passed along to the image processing electronics for processing and analysis .

Image Processing Electronics:
Image processing is performed by embedded algorithms on a per channel basis. The following block diagram illustrates the basic processing architecture for each channel.

The processing algorithm is embedded in the processing PLD. The microprocessor has a bidirectional path being able to randomly access the algorithm parameters, as well as pro- gram a new algorithm into the PLD as required by the user. Raw pixel data and associated timing and control signals are also connected to input pins into the processing PLD,.For storage and subsequent readout, algorithm processed data is output along with a write control signal to FIFO memory. 8:1 multiplexing of the data is achieved by using FIFO memory. Readout control is accomplished by the microprocessor/FIFO readout control card.
The Microprocessor/FIFO readout control circuit board acts as the master controller for the smart camera. FLASH memory is used to store microprocessor code and PLD algorithm code. In-system programmability is achieved because all PLD devices in the image processing section of the camera are SRAM based.
Image Processing Algorithms:
Many types of image processing algorithms can be embedded within the camera, since the video processing modules are completely in-system programmable. As an example, a static grey scale thresholding algorithm is presented below.
Static Grey Scale Thresholding:
In static thresholding, an upper and a lower bound are established around what is considered a normal value. Image data that falls within the boundary window is considered nor- mal non-interesting data. Image data that falls either above or below the boundary window is considered data of interest. Considering we are dealing with an 8-bit digital camera, the normal, upper and lower boundary values are seen to be digital numbers (DN) on a scale of 0 to 255 Gray scale. Imagine that a product is being inspected for defects and the grey scale level of non-defective product is 85 DN, and the upper and lower boundary values have been set to +/- 15 DN.
All image data that fell within the bounds of 70 DN to 100 DN would be considered non-interesting and would not be transmitted out of the camera. Image data that fell below 70 DN and above 100 DN would be considered interesting and would be transmitted out of the camera. Substantial data reduction is achieved since only some of the data will fall outside of the established boundaries. It is important to note that all of the Ëœdata of interestâ„¢ is transmitted out of the camera and thus data reduction is achieved where all of the grey scale information is preserved. This type of algorithm is illustrated by the image shown in figure 6.
For later display and analysis each pixel must be given an address such that an image can be reconstructed by the frame grabber since an algorithm of this type produces non- continuous data. The static thresholding algorithm requires three parameters as follows an upper bound, a lower bound, and a centre value. The determination of the centre value is essential to this type of algorithm, and the acceptable band between the upper and lower bound. The static thresholding algorithm is expressed as follows:
Embedded Image Processing Algorithms:
The algorithms are embedded in hardware with a PLD/microcontroller combination and operate at a 20MHz data rate per channel. The effective processing rate is 40MHz because each image processing PLD can process two channels of image data. With dedicated DSP controllers such data processing rates could be difficult to achieve. Microcontroller also can directly control the algorithm without host computer intervention, since it has access to the image data .
Smart Camera System:
A vision system for web inspection is presented below where a maximum of twenty 2048 pixel high sensitivity line scan smart cameras are networked together to a single host computer and frame grabber. A block diagram of the system is shown in figure 7.
System Overview:
The system shown consists of up to twenty 2048 pixel high sensitivity line scan smart cameras housed within a camera enclosure mounted above the web. Transmissive illumination is provided since illumination source is mounted beneath the web. Routed through two cabinets are the data, control, and power lines to/from the cameras. The system makes use of fibre optic technology for transmission of data and control signals thus allowing the inspector station to be located remotely at a distance of up to 100m.

The system also provides input and output to the plant control system. Data from the cameras is acquired by the frame grabber, assembled into images, and then transferred to the host computer real time display on the defect monitor, and stored to a database residing on the file server via an Ethernet connection. Subsequent analysis of the data is performed at the analysis workstation with analysis software that allows extraction of data from the database for creation of reports. All system and analysis software is multithreaded and provides real time data access and display. Via a modem connection the system is also operable remotely. To ensure smooth and constant illumination of the web the system software also controls the illumination source with a fuzzy logic control scheme.
Looking Ahead:
The vision industry is rapidly moving away from the video camera/frame grabber systems of the twentieth century to a new generation of smart-camera-based systems for the 21st century. These 21st century smart-camera systems will perform real-time, pixel-data ex- traction and processing operations within the camera at extremely high speeds and at a cost, which is considerably less than required today for comparable capabilities.
Eventually, complete vision-processing-systems-on-a-sensor-chip will be available.Components in smart cameras will undoubtedly change due to the push from semiconductors and new microprocessors coming onto the market. The trends for the next year will be towards megapixel sensors, higher resolution, faster processing power, and colour. A typical standard CCD based camera has a matrix of 480,000 pixels, however the new megapixel cameras offer at least 1,000x1,000 pixels “ or 1 million pixels. Some manufacturers already offer cameras of 2 million pixels.
Future Applications:
To date, exploitation of smart camera technology has been mainly for industrial vision systems, but a crossover is just starting to take place. Smart camera technology will begin to enter new applications, for example, in the security and access control markets, in the automotive industry, for collision avoidance, and even “ one day for the toy industry.
Even our automobiles may soon be outfitted with miniature eyes. Built into a cruise con- trol system, for instance, such a camera would suddenly alert the driver if it noted a rapidly decelerating vehicle. The cameras could also take the place of the rear view and side-view mirrors, thereby eliminating dangerous blind spots and - in the event of an accident - recording the seconds prior to a collision.
Another example would be with intelligent lifts. An office block, with many lifts and floors, may see a lot of people travelling up and down between floors, particularly at high- traffic times such as early morning or end of the working day. At the moment, lifts are called by somebody pressing a button and putting in a request for the lift to stop at a particular floor. Connected with smart camera technology, lifts could be routed on demand, working intelligently, stopping only when there was a preset number of passengers wait- ing at a floor “ and missing out a floor if too many people were waiting to meet the maxi- mum capacity of the lift.
Looking into the future, we can foresee an infinite number of applications for the smart camera; in fact, as many as there are potential image processing uses.
At the development stage, we can evaluated the algorithms according to accuracy and other familiar standards. However, an embedded system has additional real-time requirements:
1. Frame rate:
The system must process a certain amount of frames per second to properly analyze motion and provide useful results. The algorithms we use as well as the platforms computational power determine the achievable frame rate, which can be extremely high in some systems.
2. Latency:
The amount of time it takes to produce a result for a frame is also important because smart cameras will likely be used in closed-loop control systems, where high latency makes it difficult to initiate events in a timely fashion based on action in the video field.
Moving to an embedded platform also meant that we had to conserve memory. Looking ahead to highly integrated smart cameras, we wanted to incorporate as little memory in the system as possible to save on both chip area and power consumption. Gratuitous use of memory also often points to inefficient implementation.
Recent technological advances are enabling a new generation of smart cameras that represent aquantumleapin sophistication. Whiledigital cameras capture images, smart cameras capture high level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection,surveillance, motion analysis,and facial identification. Video processing has an insatiable demand for realtime performance. Smart cameras leverage very large scale integration to meet this need in a low cost, low power with substantial memory. Moving well beyond pixel processing and compression, these VLSI systems run a wide range of algorithms to extract meaning from streaming video. Recently, Princton University researchers developed a first generation smart camera system that can detect people and analyze their moment in realtime.
[1]. Advanced Imaging Europe Magazine: The intelligent camera, October (2002) 12 “ 16.
[2].Wintriss Engineering Corporation: Year 2002 Smart Cameras.
[3]. Smart Camerasvs.PC-based Machine Vision Systems,, (2002).
[4]. Longbottom, D.: Latest Developments in Sensor and Camera Technology, Alrad Instruments Ltd., White paper.

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Smart Camera

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Smart cameras are cameras that can perform tasks far beyond simply taking photos and recording videos. Thanks to the purposely built-in intelligent image processing and pattern recognition algorithms, smart cameras can detect motion, measure objects, read vehicle number plates, and even recognize human behaviors. They are essential components to build active and automated control systems for many applications, and they will play significant role in our daily life in the near future. This paper aims to provide a first comprehensive review of smart camera technologies and applications. Here, we analyse the reasons behind the recent rapid growth of the smart cameras, discuss different categories of them and review their system architectures. We also examine their intelligent algorithms, features and applications. Finally we conclude with a discussion on design issues, challenges and future technological directions.
Keywords: smart cameras, pattern recognition, machine vision, computer vision, video surveillance, embedded systems.


What is a smart camera? Different researchers and camera manufacturers offer different definitions. There does not seem to be a well-established and agreed-upon definition in either the video surveillance or machine vision industries, probably the two most active and advanced applications for smart cameras at present. For the purpose of this paper, we define a smart camera as a vision system in which the primary function is to produce a high-level understanding of the imaged scene and generate application-
captures video of a scene, detects motion in the region of interest, and raises an alarm when the detected motion satisfies certain criteria. In this case, the ASIP is motion detection and alarm generation.

The Rapid Growth of Smart Cameras

Coming of Age of CMOS Image Sensors

The advent of CMOS image sensors (CIS) in late 1990s played an important role in the development of smart camera technology and systems, and has potential to make smart camera smaller, cheaper and more pervasive. Compared to CCD, CIS have several advantages which make them excellent candidates for smart camera front-end. These include smaller size, cheaper manufacturing cost, lower power consumption, the ability to build a camera-on-a-chip, the ability to integrate intelligent processing circuits onto the sensor chip, and significantly simplified camera system design.

Research in Computer Vision and Pattern Recognition

What makes a camera smart is the intelligent ASIP - the application-specific information processor built into the camera system. The advancement in academic and industrial research in real-time image processing and understanding, pattern recognition, machine learning, computer vision and video communication continues to provide a large library of intelligent algorithms for use by smart cameras for different applications. As an example, Intel’s OpenCV (Open Source Computer Vision) Library [3] has been very popular with academic researchers and students working on smart camera project and implimentations. Every year, numerous international journals, conferences and workshops give researchers world-wide forums to present their innovative work in areas such as computer vision and pattern recognition. A lot of the work presented can be seen as embryos of future smart cameras. Recently, first ever international conferences and workshops have been held focusing on the design of embedded vision systems.

Socio-Economical Drivers

Thanks to Moore’s law, semiconductor chips and computer hardware continue to shrink in size, reduce in cost and gain in performance. This has driven the prices of cameras, frame grabbers and computers down and made smart camera systems, especially PC-based systems, more affordable to research and development on one hand and to the market and end-users on the other. As hardware constraints (cost-wise) are lifted, software developers have more freedom to write "smarter" algorithms.
One of the most significant developments in surveillance and security industries in the last several years has been the wide use of CCTV (Closed Circuit Television) cameras and their impact on crime, terrorist attacks, and on the general public. It is noticeable that after the 9/11 event in the US, video surveillance has received more attention not only from the academic community

Digital Video Surveillance

The first generation of CCTV cameras (1980s-1990s) was mostly analog cameras with limited functionality and high cost. Digital CCTV cameras and the use of DVR (Digital Video Recorders) represented the second generation (2G, 1990s-now). Digital CCTV cameras built using CCD and CMOS image sensors provide better video quality, some intelligent functions such as motion detection, electronic PTZ (Pan-Tilt-Zooming), and networking. The 2G CCTV systems have become mass market products, fuelled by improved affordability and society’s increasing concerns over safety and security. According to estimates made in 2004 by market research firm Datamonitor [5], digital video surveillance is a high-growth segment within the overall surveillance market estimated at 55% CAGR (Compound Annual Growth Rate) between 2003 and 2007. In dollar terms, between 2003 and 2007 the market will grow from US$1.3bn to US$7.4bn globally.
However, the 2G CCTV systems are not “smart” enough to help prevent crimes or terror attacks, even though they proved very useful in post-event identification of crime perpetrators. The 2G CCTV systems are mostly not automated systems and rely strongly on trained security personnel to perform image analysis, object tracking and identification. The increasing number of cameras makes this difficult for real-time analysis by security personnel. Network bandwidth is another important issue affecting real-time processing needed for crime prevention. The intelligent video surveillance system (IVSS) (also called the third generation CCTV system) will try to provide solutions to these problems. Smart cameras will be one of the fundamental building blocks of the IVSS, making it possible to build and deploy automated, distributed and intelligent multi-sensory surveillance systems capable of tracking humans and suspected objects, analyzing human behaviors, and etc. Many market research firms have predicted significant growth in intelligent video systems and smart cameras.

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