This article presents the shortcomings of analog charge-coupled devices (CCD) and the benefits of wide dynamic range imaging sensors for transportation. It shows how to leverage wide dynamic range camera in transportation security.
Transportation settings present challenging lighting, temperature and motion capture conditions for surveillance cameras. New image sensor technologies address these challenges to deliver excellent image quality with wide dynamic range, high signal-to-noise ratio and progressive scan with a global electronic shutter. Choosing cameras with new image sensor technologies provides a simple and effective method for integrators to deliver better transportation security systems to their clients.
Increased Need and Government Spending to Improve Transportation Security Systems
Since Sept. 11, more than US$24.5 billion in federal grants have been spent on improving security in transportation systemsairports, seaports, rail and transit systems. While airport security has by far received the most funding to date, more attention by the U.S. Congress is now being focused on rail and transit security.
Recent events such as the coordinated bombing attacks on Madrid trains in 2004, the London bus bombing attacks in July of 2005, the devastating attacks in Mumbai, India in July 2006, and the unexploded bombs found in two German railway stations in Dortmund and Koblenz in the summer of 2006, provide evidence of the unaddressed vulnerabilities of these systems.
To help transit agencies combat these threats, the Department of Homeland Security (DHS) and other governmental agencies throughout the world have identified CCTV and specifically intelligent video systems made up of surveillance cameras, intelligent video software, video management applications, and digital recording devicesDVR and NVRas critical technology infrastructure. Smart surveillance systems are specifically mentioned within the DHS Transit Security Grant Program as potential funded solutions. It is clear that DHS supports the vision that smart surveillance systems hold great promise in improving situational awareness beyond traditional CCTV systems.
Wide Dynamic Range Improves Smart Surveillance Systems
Smart surveillance systems can provide a tremendous "force multiplier" effect, allowing fewer security personnel to effectively cover a greater area. But getting these complex systems to optimally function with high accuracy requires careful calibration with the back-end intelligent video packages and the front endthe surveillance camera itself. The quality of the image captured through the camera has an enormous impact on the accuracy of intelligent video software, and on other performance metrics such as network bandwidth usage and video storage capacity.
Image quality is often overlooked as a key area for overall system performance because many integrators still struggle with the shortcomings of analog CCD sensor based cameras. A new category of surveillance cameras, those with wide dynamic range (WDR), is providing integrators with the opportunity to deliver improved image quality. Improvements can be measured in the ability to capture scenes that would not be possible with traditional analog CCD cameras and provide accurate scene analysis from intelligent video applications. Leveraging wide dynamic range cameras within transportation applications provide a simple and effective method for integrators to deliver more value to their customers.
Intelligent Video Algorithms and the Importance of Image Quality
Intelligent video applications contain algorithms that are performing real time image processing functions. These algorithms examine compressed or uncompressed video to differentiate between foreground and background, still and moving objects, what is and is not a target, and even if something is a video artifact or an event of interest.
Inherent in the ways that these algorithms function is a level of estimation based on what the algorithm "sees." A high quality image with correct exposure for the entire scene in the highlights and shadows, high color fidelity, sharp imagery with maximum detail and very low video noise will produce the most accurate results from intelligent video applications. More data and a higher quality video signal means the levels of estimation can be reduced. Less estimation results in a reduction of false alarms and an overall improvement in the accuracy of the scene analysis.
The ability for video images to be efficiently compressed, moved over IP networks, and stored, with sufficient quality is another important aspect of smart surveillance solution performance. In order to maintain viewable quality, the level of compression often has to be compromised with cameras that can only provide lower quality images. A lack of image compressibility has a ripple effect, increasing storage requirements and consuming more network bandwidth and potentially limiting the number of cameras that can be implemented.
Transportation Environments Make Capturing Quality Images Difficult
Capturing quality images in transit and rail applications is difficult due to typically extreme environmental conditions. Two main environmental factors that present a challenge for integrators are:
Extreme lighting conditions caused by the physical layout of rail and transit facilitiesrail yards, indoor/ outdoor transit stations, inaccessible underground subways and the installation constraints on mobile implementations such as on buses, trains, and light rails to capture windows and doorways.
Extremes in temperature caused by implementation location, size constraints and form factor requirements.
Because transit stations are designed to provide convenient, open access for passengers and aesthetic appeal at the same time, designs often incorporate large amounts of glass, and open expansive areas, especially in indoor/outdoor boarding areas. While they may be pleasing to the eye, these areas create many problems to integrators of smart surveillance systems.
For example, installed surveillance cameras must deal with constantly changing lighting conditions exacerbated by the design of the stations to capture faces on train platforms or entryways and insure train doorways are clear of arms and limbs. Aluminum shelled trains and shadowed overhangs, contrasted by a bright sunlight sky, only magnify the extremes.
Analog CCD cameras struggle in these types of implementations. Due to limited dynamic range of CCD sensors, in high lux settings oversaturation of the sensors is easily reached, resulting in a number of detrimental video artifacts. These artifacts range from blooming where there is loss of color fidelity, or inability to distinguish one color from another, and smearing of the image where large portions of the captured images are lost appearing as a white band completely lacking in any detail.
Savvy installers have had no choice but to compromise in the implementation due to these deficiencies. Stopping down the iris or using BLC (back light compensation) on CCD based cameras to better manage these hard-to-capture scenes only provide a bandage to the problem. Both of these techniques knock down the highlights in an image helping to manage the source of blooming and smearing, but do so with a loss of luminance and detail especially in the lowlight areas (e.g. they turn black). Less light coming into the camera also means less data. Less data, not only results in a sub-optimized picture, but also results in less accuracy of the intelligent video solution further down the chain.
Harsh environments due to extreme temperature fluctuations present another challenge for transit and rail system security camera performance. Heat is a significant issue whether cameras are pole mounted in rail yards in direct sunlight for perimeter security or installed in underground rail tubes with little ventilation.
Heat causes reliability issues with mechanical mechanisms, such as DC iris lenses, and has detrimental effects on video quality. Analog CCD image sensors, like other semiconductors become less efficient as the temperature rises.
Heat build up on the sensor chip causes cross-talk between analog circuits and is the main source of video noise. Video noise has a detrimental effect on picture quality, as it increases the latency of intelligent video applications and requires more computing resources either in an intelligent camera, or on a server. The intelligent video applications have to work harder to compensate for the noise in the video signal that may "look like" anomalous behavior. Consequently, intelligent video applications must apply filtering techniques that take up precious computing time to compensate for the noisy images captured analog CCD image sensors.
Moving TargetsLight and Motion Challenges
Mobile applications may be the most challenging for analog CCD-based cameras because their inherent limited dynamic range impedes their ability to handle rapidly changing lighting conditions. Cameras for onboard door facing cameras for example, must manage proper video exposure from fluorescent lighted interiors with tinted windows, then quickly adapt to a door opening and direct sunlight, where the light values could be up to 20,000 times brighter than the interior. Throughout this extreme dynamic lighting condition, the camera must still be able to perform the key imaging requirements, such as capturing the faces of all who enter the vehicle with correct color, white balance, etc.
Their usefulness is also limited in other applications such as forward facing cameras positioned to capture traffic events and driver response. Interlace artifacts that appear as "sawtooth" edges, show up with objects in motion.
More detrimental to image quality are the sensors' inability to properly render color and luminance in these lighting conditions. For example, such inaccuracies can make a difference in determining the status of a signal light in the case of an accident or other traffic event.
New Sensor Architectures Enhance Image Quality
Market awareness of these deficiencies has been growing and is evident in the rise of wide dynamic range cameras as the fastest growing category of surveillance cameras. A casual survey of the major camera vendors reveal that WDR truly has hit the mainstream; nearly all have at least one WDR camera in their line, and many have multiple WDR models.
New sensor technology breakthroughs are one important factor in fueling this shift. Digital Pixel System (DPS) technology moves the conversion of captured light (analog) to digital format at the source of capture. This fundamental change allows cameras with this technology to avoid many of the pitfalls of analog CCD sensors described in this article.
Integrators that implement transportation systems should consider cameras with Digital Pixel System technology within their evaluations as they provide significant improvements in a number of sensor characteristics that are important to transportation based implementations:
Wide dynamic rangerefers to a camera's ability to capture images with the highest ratio between highlights and shadows. This means not only being able to handle high lux scenes without oversaturation artifactsblooming and smearingbut also an ability to perform in lowlight conditions as well. WDR is measured in decibels (dB) and as a general rule, a camera that provides 100dB or greater is ideal for transportation environments.
Signal to noise ratiowhile SNR provides a measure of the level of video noise, it is also the type of noise in the video signal that is one of the largest determinants of how well captured images will look and compress. Digital Pixel System image sensors are inherently low noise because the conversion to digital format takes place at the pixel level and thus can be controlled independently for saturation. DPS technology also supports nondestructive readout of the pixel, which avoids random background noise that is detectable with the human eye. This random noise also makes a difference in image compression. Ten times the difference in compression ratios is not uncommon, especially when comparing images captured at night with high video gain. Compression ratio in turn has an impact on the overall cost of operations.
Progressive scan with global electronic shuttersimply put will provide cleaner images without interlace or motion artifacts compared to analog CCD imagers. Cleaner, artifact-free images containing more data will yield more accurate results from intelligent video applications.
When integrating surveillance solutions and architectures for transportation applications, one must provide imaging solutions that can readily adapt to varied environments, not all of which can be controlled. Integrators are best served by deploying camera solutions that are not limited by the camera's image sensor to require compromises in location, aiming or mounting.
To enable installers with the most freedom and flexibility to deploy these solutions, installers should look to cameras with newer all digital sensor solutions that can deliver high overall image quality, with low noise, color fidelity and the widest dynamic range possible. Choosing camera solutions with these parameters will provide superior visual images and improve overall performance of today's intelligent surveillance solutions.