Television – Special applications – Observation of or from a specific location
Reexamination Certificate
1997-11-06
2002-06-25
Britton, Howard (Department: 2613)
Television
Special applications
Observation of or from a specific location
C340S937000, C348S164000, C348S169000, C382S104000
Reexamination Certificate
active
06411328
ABSTRACT:
BACKGROUND
Improving Transportation Systems
The freeways and other roadways that constitute a major portion of the United States transportation system are being required to carry increasing amounts of traffic while funds for new construction and upgrades are limited. As a result, much attention and effort has been focused on methods of increasing the capacity, safety, and utility of existing road systems by early detection of accidents and rapid corrective action to restore normal traffic flow.
The Intelligent Transportation Systems (ITS's) initiative has provided development incentives and testbeds for sensor-based systems that can detect significant changes in traffic flow patterns and infer the reasons for the changes. While many traffic management systems are focused on the detection and control of congestion on freeways, a strong emphasis exists within the ITS initiatives to provide for quick detection of traffic-related incidents such that injured motorists may receive potentially lifesaving medical attention more rapidly and so resulting traffic congestion and delays may be mitigated by more timely identification and clearing of the incidents.
Typically, ITS freeways have been instrumented with inductive loop sensors in the road surface or color video cameras mounted overhead as described in U.S. Pat. No. 4,847,772, Jul. 11, 1989 (Michalopoulos, et al.), incorporated herein by reference. More recently described systems use infrared sensors alone or in combination with color video cameras as in U.S. Pat. No. 5,416,711, May 16, 1995 (Gran et al.), incorporated herein by reference. Camera(s) and/or inductive loop sensors are typically connected to a control center where computer algorithms operate on vehicle presence and/or activity as inferred by changes in a video image, bright points or “hot spots” in an infrared image or by currents induced in inductive loop sensors. Various parameters of the objects and/or events detected are processed mathematically and/or evaluated by human operators to detect unusual events such as slowdowns, stoppages or erratic traffic flow. These detected events are in turn used to infer the presence of one or more traffic-related incidents (that is, precipitating causes) on the roadway and either initiate automatic response sequences or alert human operators who can then direct video cameras (equipped with pan, tilt and zoom controls) for detailed observation and verification of an incident before taking corrective action. Most incidents are currently identified by operators observing video images or inferred by computational processing of the outputs of inductive loop detectors to determine the presence of materially altered traffic flow (frequently resulting in congestion) on the freeway. Any congestion so determined ostensibly results from the presence of an incident. Traffic condition monitors using inductive loop detectors at reasonable intervals of ¼ mile or more have been in use for many years, while camera-based systems are more recent.
The relatively small number of visible spectrum and/or infrared camera-based systems currently in use essentially provide the same information as an inductive loop detector array, but they do it more quickly and with generally greater resolution. Camera-based systems, however, are usually costly and complex, and they have limited range. Current camera-based vehicle presence sensors are basically template comparators or hot spot detectors (as in Gran et al.) which operate to determine whether or not a vehicle is within a specified location in a camera image; they are typically used where inductive loop detectors cannot conveniently be installed. Substantially all commercially available combinations of inductive loop and camera-based vehicle sensors for detecting traffic incidents suffer from limitations in performance, resolution, reliability and sensitivity.
General Sensor Limitations:
Performance limitations for inductive loop and camera-based vehicle sensors currently used as traffic incident detectors often include slow responses, missed incidents, false alarms, and uncertainty as to what event is detected. Today, most traffic incident notification is achieved within traffic monitoring systems by monitoring or using police dispatch networks, even when inductive loop and camera-based vehicle detector systems are in place.
Vehicle presence and, in some cases, vehicle speed and density estimates derived from inductive loop and/or camera-based vehicle detectors have historically been poor indicators of traffic incidents. These systems frequently operate to detect traffic congestion and alert an operator to look for an incident. On many (even most) occasions, there are no determinable causes for congestion to be found because the system has alerted on recurrent congestion or the incident causing the congestion was transient in nature (such as a car stopped for a moment) and the incident has disappeared before the congestion is detected. In some cases, the precipitating event, such as a pedestrian or a separated tire tread in the roadway, remains completely invisible to the sensors employed at all times. Such common traffic hazards are not reliably detected by any known system currently in use because they are not hot enough to be seen by hot spot infrared sensors and/or because they do not activate inductive loop sensors and/or because their visible spectrum images are too faint to be detected by the color video cameras in use. Some currently-described infrared sensors are even designed specifically to reject people in an image (see Gran et al., describing an infrared sensor system which operates in the 2-5 micron wavelength range for high target emission and high atmospheric transparency).
Other impediments to reliance on camera-based systems include variations between day and night lighting conditions, rain, fog, and other inclement weather conditions, optical distortion due to thermal air currents over the hot road surface, differences in image size of a vehicle depending on its distance from the camera, differences in the speed of vehicles, and possible vibration of cameras mounted on supports over the roadway. While vision-based vehicle detectors can be sensitive to nonmetallic objects, they are also sensitive to shadows and are subject to visibility limitations caused by fog, snow, rain, smoke, etc., and by very low ambient light levels.
Camera-based vehicle detectors are also very sensitive to alignment. The associated cameras cannot be moved or used for surveillance because lengthy and sophisticated procedures are required to re-align the system after even slight movement. This phenomenon results in significant maintenance expense for this type of vehicle detector after even slight camera pole movement (sometimes caused by other utilities that use the same pole). The effects of material creep, metal fatigue, warping, nearby construction, or temperature changes will often result in the need for a lengthy re-alignment procedure.
Sensor Resolution:
Poor resolution of vehicle data along a roadway is one of the major factors contributing to poor performance of current systems based on inductive loops, hot spot infrared detectors, and vision-based vehicle detectors. Placement of inductive loop detectors at closer intervals than ¼ mile has proven impractical, and many applications involve spacings of ½ mile or more. Infrared cameras for detecting hot spots (see Gran et al.) do not easily resolve relatively distant and thus closely-spaced hot spots. Although Gran et al. describes the use of interchangeable lenses to provide the appropriate field of view coverage, such a system can not realistically be considered for substantially real-time applications. And adding more sensors (such as cameras) also has limited utility because each vehicle detector location has associated with it a surface enclosure with power, processing, and communications equipment requirements.
Sensor Reliability:
Many inductive loop detectors in a typical system are not working at any given time. To a lesser
Franke Ernest A.
Kinkler Ernest S.
Magee Michael J.
Seida Steven B.
Baker & Botts L.L.P.
Britton Howard
Southwest Research Institute
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