Abstract:
A video-based fire detection system receives video data comprised of a plurality of individual frames, and determines based on the video data the ability of the system to detect the presence of fire. The system includes a video recognition system connectable to receive the video data and to calculate one or more background features associated with the video data. Based on the calculated background features, the video recognitions system assesses the ability of the video-based fire detection system to detect the presence of fire. The system includes one or more outputs operably connectable to communicate the results of the assessment made by the video recognitions system.

Description:
BACKGROUND 
       [0001]    The present invention relates generally to computer vision and pattern recognition, and in particular to methods of assessing the ability of a video-based system to detect the presence of fire. 
         [0002]    The ability to detect the presence of fire is important on a number of levels, including with respect to human safety and the safety of property. In particular, because of the rapid expansion rate of a fire, it is important to detect the presence of a fire as early as possible. Traditional means of detecting fire include particle sampling (i.e., smoke detectors) and temperature sensors. While accurate, these methods include a number of drawbacks. For instance, traditional particle or smoke detectors require smoke to physically reach a sensor. In some applications, the location of the fire or the presence of ventilated air systems prevents smoke from reaching the detector for an extended length of time, allowing the fire time to spread. A typical temperature sensor requires the sensor to be located physically close to the fire, because the temperature sensor will not sense a fire until it has spread to the location of the temperature sensor. In addition, neither of these systems provides as much data as might be desired regarding size, location, or intensity of the fire. 
         [0003]    A video-based fire detection system provides solutions to some of these problems. In particular, video-based systems can detect the presence of fire prior to physical evidence of the fire (e.g., smoke particles) reaching the video detector. However, the video-based fire detection system presents challenges not encountered in traditional sensors. For instance, the ability of a video-based system to detect the presence of fire depends, in part, on the environment in which the video detector is operating. In addition, problems associated with video quality degradation may inhibit the ability of the system to accurately detect the presence of fire. 
         [0004]    For these reasons, it would be beneficial to develop a method of assessing the ability of a video-based system to accurately detect the presence of fire. 
       SUMMARY 
       [0005]    Described herein is a method of initializing a video-based fire detection system to detect the presence of fire. The method includes acquiring video data comprised of individual frames from a video detector and calculating background features associated with one or more of the individual frames. Based on the calculated background features, the method assesses the ability of the video-based fire detection system to detect the presence of fire. An output is generated that indicates the ability of video-based fire detection system to detect the presence of fire. 
         [0006]    Another embodiment of the present invention describes a method of monitoring the ability of video-based fire detection system to detect the presence of fire. The method includes calculating video-quality features associated with individual frames during operation of the video-based fire detection system. The method further includes detecting video quality degradation that adversely affects the ability of video-based fire detection system to detect the presence of fire based on the calculated video quality features. In response, an output is generated that indicates the assessed ability of the video-based fire detection system to detect the presence of fire. 
         [0007]    Another embodiment describes a video-based fire detection system that includes one or more inputs operably connectable to receive video data comprised of a plurality of individual frames from one or more video detectors. The video-based fire detection system includes a video recognition system connectable to receive the video data and to provide an output assessing the ability of the video recognition system to detect the presence of fire. In particular, the video recognition system calculates one or more background features associated with each individual frame, and assesses the ability of the video-based fire detection system to detect the presence of fire based on the calculated background features. The system also includes outputs operably connectable to indicate the ability of the video recognition system to detect the presence of fire. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  is a block diagram of an exemplary embodiment of a video-based fire detection system of the present invention, including video detectors, a video recognition system, and a plurality of outputs. 
           [0009]      FIG. 2  is a block diagram of functions performed by the video recognition system in assessing the ability of the video-based fire detection system to detect the presence of fire. 
           [0010]      FIGS. 3A and 3B  are sample images illustrating the result of analysis performed by the video recognition system in assessing the ability of video-based fire detection system to detect the presence of fire. 
           [0011]      FIGS. 4A and 4B  are sample images illustrating the result of analysis performed by video recognition system in assessing the ability of video-based fire detection system to detect the presence of fire. 
           [0012]      FIG. 5  is a block diagram illustrating functions performed by the video recognition system to monitor the quality of video data provided by the video detectors. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    The present invention describes a system and method for analyzing the ability of a video-based fire detection system to detect the presence of fire. For instance, the present invention may be used during installation of the video-based fire detection system to assess the ability of the system to detect the presence of fire. The analysis may include computing many of the same features used by the video-based system in detecting the presence of fire, and analyzing these features to determine whether based on the environment in which the video detector is operating, the video-based fire detection system will be capable of detecting the presence of fire. For instance, in situations in which the background includes very little color (e.g., in a tunnel), there may be insufficient color data available to detect the presence of fire (in particular, the presence of smoke). By providing feedback at the installation stage, steps can be taken to modify the orientation of the camera, or the overall background to improve the ability of the video-based fire detection system to detect the presence of fire. 
         [0014]    In addition, the present invention may be used to assess the ability of video-based fire detection system to detect the presence of fire during the operational stage (i.e., after installation), in which the system is actively being used to detect the presence of fire. During this stage, analysis is performed on the acquired video input to detect video quality degradation that inhibits the ability of video-based fire detection system to detect the presence of fire. For example, if the video detector becomes out of focus over time, then the video input provided by the video detector may be sufficiently blurry to prohibit the video-based fire detection system from detecting the presence of fire. By automatically monitoring the quality of the video input provided for analysis, problems associated with the video data may be identified and corrected. 
         [0015]    Throughout this description, the term fire is used broadly to describe the presence of flame and/or smoke. Where appropriate, specific embodiments are provided that describe the detection of either flame or smoke. 
         [0016]      FIG. 1  is a block diagram illustrating an exemplary embodiment of video-based fire detection system  10 , which includes at least one video detector  12 , video recognition system  14 , and one or more of a plurality of outputs, including user display  16 , video quality alarm  18 , and fire alarm  20 . Video images captured by video detector  12  are provided to video recognition system  14 . The provision of video by video detector  12  to video recognition system  14  may be by any of a number of means, e.g., by a hardwired connection, over a shared wired network, over a dedicated wireless network, over a shared wireless network, etc. Hardware included within video recognition system  14  includes, but is not limited to, a video processor as well as memory. Software included within video recognition system  14  includes video content analysis software capable of performing the functions illustrated. The provision of signals by video recognition system  14  to user display  16 , video quality alarm  18 , or fire alarm  20  may be by any of a number of means, e.g., by a hardwired connection, over a shared wired network, dedicated wireless network, over a shared wireless network, etc. 
         [0017]    Video detector  12  may be a video camera or other type of video data capture device. The term video input is used generally to refer to video data representing two or three spatial dimensions as well as successive frames defining a time dimension. In an exemplary embodiment, video input is defined as video input within the visible spectrum of light. However, the video detector  12  may be broadly or narrowly responsive to radiation in the visible spectrum, the infrared spectrum, the ultraviolet spectrum, or combinations of these broad or narrow spectral frequencies. 
         [0018]    During operation of video-based fire detection system  10 , video recognition system  14  employs computer vision techniques to analyze the video data provided by video detector  12 . A variety of computer vision techniques are well-known in the art and may be employed alone or in combination to detect the presence of fire. In the event video recognition system  14  determines that the video data indicates the presence of smoke and/or flames, video recognition system  14  generates an output that triggers fire alarm  20 . 
         [0019]    In addition to the traditional computer vision techniques employed by video-based fire detection systems, the present invention includes computer vision techniques employed to assess the ability of video-based fire detection system  10  to accurately detect the presence of a fire. Results of the analysis are provided to user display  16  and/or video quality alarm  18 . In particular, during the installation stage, results of the analysis performed by video recognition system  14  are provided to user display  16  to allow a technician to determine in real-time the effectiveness of video-based fire detection system  10 . During the operational stage, results of the analysis performed by video recognition system  14  may also be provided to user display  16 . In addition, if analysis of the video data indicates the presence of video quality degradation, then video recognition system  14  generates an output that triggers video quality alarm  18 . 
         [0020]      FIG. 2  is a block diagram illustrating functions performed by video recognition system  14  in analyzing video data to assess the ability of video-based fire detection system  10  to detect the presence of fire. Video recognition system  14  includes a combination of hardware and software necessary to perform the functional steps shown within video recognition system  14 . 
         [0021]    In an exemplary embodiment, the functions shown in  FIG. 2  are performed during installation of video-based fire detection system  10  to detect any environmental factors that may adversely affect the ability of the system to detect the presence of fires. The functions described with respect to  FIG. 2  (as well as those described with respect to  FIG. 5 ) are in addition to functions typically performed by video recognition system  14  in analyzing video data to detect the presence of fire. Although there may be overlap between the features calculated to detect the ability of the system to detect fire, and features calculated to actually detect the presence of fire, for the sake of simplicity the functions described with respect to assessing the ability of video-based system  10  to detect fire are described as a stand-alone system. 
         [0022]    Functions performed with respect to  FIG. 2  include storing video frames to a buffer (step  22 ), calculating background features associated with each frame (step  24 ), applying decisional logic to the calculated background features to determine the ability of video-based fire detection system  10  to detect the presence of fire (step  26 ), and generating results to be displayed to a user (step  28 ). 
         [0023]    At step  22 , frames of video data provided by video detector  12  are stored to a buffer. The frame buffer may retain one frame, every successive frame, a subsampling of successive frames, or may only store a certain number of successive frames for periodic analysis. The frame buffer may be implemented by any of a number of means including separate hardware or as a designated part of a video capture card or computer memory. 
         [0024]    At step  24 , one or more “background features” are calculated with respect to each frame of video data. The term “background feature” is used generally to refer to features that characterize the environment within the field of view of the video detector. In an exemplary embodiment, video recognition system  14  calculates one or more background features characterizing the color content, spatial frequency content, edge content, motion-based content, illumination content, contrast content, and combinations thereof generated with respect to the video data. These features may also be employed by video recognition system  14  during the operational stage to detect the presence of fire. During this stage, however, these features are employed to determine whether the video-based fire detection system is capable of detecting the presence of fire in light of the environmental or background features. In addition, during the installation stage, features associated with the quality of the video data may also be generated for analysis (as described with respect to  FIG. 5 ), but in general a technician or installer of the video-based fire detection system will be capable of manually assessing the quality of the video data upon installation. For purposes of this description, the features used to assess the ability of the video-based fire detection system to detect the presence of fire at installation are described as background features. 
         [0025]    For example, color-based features and edge-based features are commonly used, to detect the presence of smoke. In particular, color-based features are often used to detect the presence of “turbulent smoke”. Video recognition system  14  calculates one or more color-based features to monitor the color content associated with a particular area, and looks for a characteristic loss of color indicative of the presence of smoke. In environments in which the background lacks color, the color-based features calculated at step  24  can be used to assess whether based on the lack of color, the algorithms typically employed by video recognition system  14  to detect the presence of smoke will be successful. 
         [0026]    Edge-based features are also commonly employed to detect the presence of smoke. In particular, edge-based features are often used to detect the presence of “obscuring smoke.” Video recognition system  14  may calculate one or more edge-based features. Once again, video recognition system  14  analyzes the edge-based features for a loss or degradation of edge-based data indicative of the presence of smoke. In environments in which the background lacks defined edges, the edge-based features calculated at step  24  can be used to assess whether based on the lack of edge data, algorithms typically employed by video recognition system  14  to detect the presence of smoke will be successful. These features, as well as others, may be similarly employed to assess the ability of video recognition system  14  to accurately detect the presence of flame. Background features may be represented as a singular value, or may be represented as a distribution that can be used in analyzing the background content. 
         [0027]    At step  26 , the background features are analyzed by decisional logic to assess the ability of video-based fire detection system to detect the presence of fire. For example, with respect to color-based features, a determination is made whether the background includes sufficient color to allow video recognition system  14  to detect the presence of fire. With respect to edge-based features, a determination is made whether the background includes sufficient edge content to allow video recognition system  14  to detect the presence of fire. Analysis of the background features at step  26  may include analysis of each feature independently, or may include analysis of the background features in combination, to determine whether the combination of available features can be used to accurately detect the presence of fire. 
         [0028]    In an exemplary embodiment, the decisional logic employed at step  26  compares the calculated background features to thresholds or constraints to assess the ability of video-based fire detection system  10  to detect the presence of fire. This may include comparing the calculated background features to thresholds defining minimum background feature requirements for the detection of fire (including different thresholds for the detection of flame and smoke, respectively) as well as additional thresholds that may be used to define various levels of capability associated with the ability of the video-based fire detection system  10  to detect the presence of fire. Decisional logic may be implemented with a variety of well-known classifiers or algorithms, including fuzzy-based inference systems, training-based systems, neural networks, support vector systems, or other well-known classifiers. 
         [0029]    At step  30 , an output is generated in response to the analysis performed at steps  24  and  26 . In an exemplary embodiment, the output may be a binary output indicating whether, based on the background features extracted, video recognition system  14  is capable of detecting the presence of fire. In other exemplary embodiments, the output is more detailed, providing a technician or operator with additional information regarding the ability of video recognition system  14  to detect the presence of fire. For example, the output may be graphical in nature, illustrating an assessment of the ability of video-based fire detection system to detect the presence of fire within each area of the field of view of video detector  12 . In another example, the output is real valued and represents the certainty or the ability of video recognition system  14  to detect the presence of fire. For instance, the real-valued output may be a percentage indicating the certainty with which the video-based fire detection system can be expected to detect the presence of fire. In another example, the output includes recommendations on how to improve the ability of fire detection system  10  to detect the presence of fire. For example, recommendations may relate to the orientation and/or position of the camera as well as recommendations regarding physically modifications that may be made to the background to improve the ability of video-based fire detection system  10  to detect the presence of fire. 
         [0030]      FIGS. 3A and 3B  are examples illustrating analysis performed by video recognition system  14  in assessing the ability of the video-based fire detection system to detect the presence of fire (in particular, smoke). In particular,  FIG. 3A  is a sample image received from a video detector, and  FIG. 3B  is the resulting output generated by video recognition system  14  illustrating the ability of the video-based fire detection system to detect the presence of fire. 
         [0031]    In this example, the video detector is positioned to monitor a tunnel as shown in  FIG. 3A . In  FIG. 3B , the resulting analysis generated by video recognition system  14  identifies regions that have insufficient edge and color content (illustrated by region  32 ), regions that have sufficient edge content (illustrated by cross-hatched region  34 ), regions that have sufficient color content (illustrated by cross-hatched region  36 ), and regions that have sufficient edge and color content (illustrated by cross-hatched region  38 ). 
         [0032]    The display presented to a user may be color-coded to alert the user to the status of a particular region within the field of view of the video detector. For example, regions determined to contain insufficient edge and color content (e.g., region  32 ) may be displayed to the user with a first color. Regions having sufficient edge content (e.g., region  34 ) or sufficient color content (e.g., region  36 ) may each be displayed with different color(s), and areas in which video recognition system  14  is unable to determine whether there is regions in which both the edge content and the color content is sufficient (e.g., region  38 ) may be displayed with yet another color. 
         [0033]    As a result of the analysis performed by video recognition system  14 , some regions (e.g., region  32 ) may be identified as lacking the background features necessary to detect the presence of fire (i.e., fire detection system  10  will be unable to detect the presence of fire). Other regions (e.g., regions  34  and  36 ) may be identified as having a reduced capability to detect the presence of fire. For instance, region  34  lacks sufficient color content to detect the presence of fire, but does provide sufficient edge content to detect the presence of fire. In particular, due to the lack of color content in region  34 , video-based fire detection system  10  may be unable to detect the presence of turbulent smoke indicative of fire. Region  34  may therefore be classified as providing a reduced or somewhat diminished ability to detect the presence of fire. Likewise, region  36  lacks sufficient edge content to detect the presence of fire, but does provide sufficient color content to detect the presence of fire. In particular, due to the lack of edge content in region  36 , video-based fire detection system  10  may be unable to detect the presence of obscuring smoke indicative of fire. Once again, region  36  may be classified as providing a reduced or somewhat diminished ability to detect the presence of fire. 
         [0034]    In this particular example, analysis indicates that large areas of the tunnel (illustrated by region  32 ) have relatively little edge or color content that can be used to detect the presence of fire. Based on the output generated by video recognition system  14 , steps can be taken to either re-orient the video detector to locate a background having sufficient edge or color content or physically alter the background to add additional edge or color content to those areas identified as insufficient. In the example shown in  FIGS. 3A and 3B , reflective lines may be added to portions of the background identified as having insufficient edge content. 
         [0035]      FIGS. 4A and 4B  illustrate another example of analysis performed by video recognition system  14  in assessing the ability of the video-based fire detection system to detect the presence of fire. In this example, regions identified by video recognition system  14  as containing insufficient color and edge content are illustrated by cross-hatched region  40 , regions identified as containing sufficient edge content are illustrated by cross-hatched region  42 , regions identified as containing sufficient color content are illustrated by cross-hatched region  44 , and regions identified as containing both sufficient color and sufficient edge content are illustrated by cross-hatched region  46 . Based on the output generated by video recognition system  14 , steps can once again be taken to either re-orient the video detector to locate a background having sufficient color content or physically alter the background to add additional color content to those areas identified as insufficient. In this example, significant portions of the background do contain significant color content and/or sufficient edge content. In addition to the graphical output indicating the sufficiency of the color content and the edge content in the background, video recognition system  14  may also generate a value representing the certainty or the ability of video recognition system to detect the presence of fire. 
         [0036]      FIG. 5  is a block diagram illustrating functions performed by video recognition system  14  to automatically monitor the quality of the video data provided by video detector  12  in assessing the ability of video-based fire detection system  10  to detect the presence of fire. In this way, video recognition system  14  continually monitors the quality of the video data provided by video detectors  12  and automatically detects video quality degradation. In particular, video recognition system  14  calculates features that indicate a decreased ability of video-based fire detection system  10  to detect the presence of fire. In response to a determination that the ability of video-based fire detection system to detect the presence of fire has degraded, a notification signal or alarm is triggered to alert supervisors of the system. 
         [0037]    In the embodiment shown in  FIG. 5 , functions performed by video recognition system  14  include storing video frames to a buffer (step  48 ), calculating video quality features associated with each frame (step  50 ), optionally storing one or more of the video quality features calculated with respect to each frame (step  52 ), applying decisional logic to detect video quality degradation that may adversely affect the ability to detect the presence of fire (step  54 ), and generating results to be displayed to a user (step  56 ). 
         [0038]    At step  48 , frames of video data provided by video detector  12  are stored to a buffer. The frame buffer may retain one frame, every successive frame, a subsampling of successive frames, or may only store a certain number of successive frames for periodic analysis. The frame buffer may be implemented by any of a number of means including separate hardware or as a designated part of computer memory. 
         [0039]    At step  50 , one or more “video quality features” are calculated with respect to each frame of video data. The term “video quality features” is used generally to refer to both the background features described with respect to  FIG. 2 , as well as other features used to assess the quality of the video data provided to video recognition system  14 . In particular, the features calculated at step  50  characterize aspects of video quality such as signal strength, noise, signal to noise ratio, on-line computable video quality metrics such as those used to detect compression artifacts, lighting sufficiency, saturation, video detector shaking or movement, video detector focus, video detector alignment, and other features associated with video quality. 
         [0040]    In an exemplary embodiment, one or more of the video quality features calculated upon installation of the video-based fire detection system or during operation of the video-based fire detections system are stored to memory or a buffer, as shown at step  52 . The stored video quality features are used as a benchmark with which to compare video quality features calculated with respect to subsequent frames of video data. 
         [0041]    At step  54 , the video quality features are assessed by decisional logic to detect video quality degradation that would affect the ability of video-based fire detection system  10  to detect the presence of fire. For example, decisional logic analyzes the video quality features calculated at step  50  to detect conditions such as excessive noise, presence of compression artifacts, insufficient lighting, over saturation, shaking of the video detector, out-of-focus, misalignment, loss of contrast, and loss of video input. Part of the analysis related to video quality degradation may include distinguishing between video quality degradation and situations indicative of fire. In an exemplary embodiment, slow changes brought on by video quality degradation are distinguished from sudden changes (typically associated with the propagation of fire) by storing video quality features over time to detect gradual changes in the features. For example, the loss of edge data associated with an out-of-focus video detector may be mistakenly classified as indicative of smoke. However, by storing and comparing video quality features associated with out-of-focus over defined intervals, the gradual or slow progression of the video detector from being in-focus to out-of-focus can be used by decisional logic to distinguish between a video quality problem and the presence of a fire. 
         [0042]    In an exemplary embodiment, the decisional logic employed at step  54  is a fuzzy-based inference system that compares the calculated video quality features to thresholds or constraints to detect video quality degradation that affects the ability of video-based fire detection system  10  to detect the presence of fire. In other embodiments, the decisional logic also employs the stored video quality metrics (e.g., baseline video quality metrics stored at installation of the system, video quality metrics calculated at defined intervals) to detect gradual changes in the quality of the video data indicative of video quality degradation. 
         [0043]    In another exemplary embodiment, video-quality metrics calculated during installation (e.g., at a time which video-quality is typically considered sufficient for detection of fire) is used to generate a target-based distribution. In an exemplary embodiment, the target-based distribution is generated by dividing an image (e.g., an image captured during installation) into discrete sub-images. For example, the image may be divided into a 3×3 grid of equally sized sub-images. A distribution associated with the video-quality feature(s) is calculated for each sub-image, and the collection of distributions defines the target-based distribution. In this way, the target-based distribution represents a benchmark that can be used to gauge the video-quality of subsequent frames of video data. 
         [0044]    A similar distribution is calculated as part of the video-quality metrics calculated at step  50  with respect to a current frame of video data. For example, the current frame of video data may be divided into a plurality of sub-images (e.g., a grid of 3×3 equally sized sub-images) and a distribution can be generated with respect to each sub-image based on one or more video-quality features. Decisional logic then compares the video quality-based distributions with the target-based distribution. In an exemplary embodiment, an entropy value is calculated based on the comparison of the background-based distribution and the target-based distribution, wherein the entropy represents the difference between the two distributions. In this way, decisional logic can assess the quality, and thus the ability of video-based fire detection system to accurately detect the presence of fire. 
         [0045]    At step  56 , an output is generated in response to the analysis performed at steps  50  and  54 . In an exemplary embodiment, the output may be a binary output indicating, based on the calculated video-quality features, whether video quality degradation has affected the ability of video-based fire detection system to detect the presence of fires. In this embodiment, the output automatically triggers video quality alarm  18  (as shown in  FIG. 1 ), alerting a supervisor or others of the detected video quality degradation. In other embodiments, additional information including the type of video quality problems detected or specific values associated with the calculated video quality features may be provided along with the triggering of the video quality alarm. In another example, the output is real valued and represents the certainty of the ability of video recognition system  14  to detect the presence of fire. 
         [0046]    In this way, the present invention provides a system and method for assessing the capability or ability of a video-based fire detection system to detect the presence of fire. This includes assessing environment factors (such as lack of color edge information, etc. in the background) as well as video quality problems (such as out-of-focus conditions, camera shaking, etc.) that may prevent video-based fire detection system from accurately detecting the presence of fire. In this way, the present invention is able to assess the ability of video-based fire detection system to detect the presence of fires. Although the present invention has been described with reference to preferred embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention.