Abstract: A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of detected objects, each of which has a signature representing features of the detected object. The match classifier matches object instances by analyzing data derived from the signatures. The match classifier determines whether the signatures match.

Abstract: Disclosed are systems and methods for reducing video communication bandwidth requirements of a video surveillance network camera system that includes network communication paths between network video cameras producing video streams of scenes observed by the network video cameras and content-aware computer networking devices analyzing by video analytics video visual content of the video streams to provide managed video representing, at specified quality levels, samples of the scenes observed. Distribution of the managed video consumes substantially less network bandwidth than would be consumed by delivery through network communication paths a video stream at the specified quality level in the absence of analysis by the video analytics.

Abstract: An automated electronic video surveillance system enables a high-resolution mega-pixel camera to capture high quality, detailed, magnified images at multiple locations, simultaneously with an overview of the whole scene. A preferred embodiment requiring no moving parts provides full 360-degree continuous viewing with up to 5× all-digital zoom capability. The system performs continuous surveillance and active resolution allocation in the form of a feedback control subsystem that dynamically allocates resources so that important details within a scene receive appropriate scrutiny, while uninteresting areas are imaged at a lower resolution.

Abstract: An intelligent lighting system employs energy efficient outdoor lighting and intelligent sensor technology in cooperation with video analytics processing. The lighting system selectively illuminates outdoor spaces and identifies and evaluates events in a scene monitored by a video camera, thereby to facilitate proactive and appropriate security responses to those events. Selective use of advanced lighting fixtures may significantly reduce costs of lighting areas that are monitored by security systems such as streets, public parks, and parking lots, while simultaneously improving security, safety, and traffic control. Energy savings alone, for a properly designed system, are estimated at 50%-90% of current usage. When combined with remote monitoring, such systems may prevent accidents and criminal activity.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A processor-implemented access control method includes receiving credential and policy directory information to configure an access controller to allow self-provisioning of the access controller through periodic, automated query of the directory by the access controller; acquiring from the directory, credential and policy information for one or more individuals who may require access; storing in a local cache the acquired credential and policy information; receiving an access request to allow an individual access; comparing the access request to the credential and policy information in the cache; and when the comparison indicates a match, granting the individual access.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of detected objects, each of which has a signature representing features of the detected object. The match classifier matches object instances by analyzing data derived from the signatures. The match classifier determines whether the signatures match.

Abstract: A system and method for segmenting a current frame of a video includes receiving a segmentation mask determined from a previous frame of the video, receiving a background model value and a background deviation value for a given pixel of a previous frame of the video, updating the received background deviation value, updating the background model value independently of the updating of the background deviation value and determining a value of a pixel of a new segmentation mask corresponding to the given pixel based on a foreground model value of the current frame, a segmenting background model value of the current frame and a segmentation threshold, the determining of the new segmentation mask defining whether the given pixel is a foreground pixel or a background pixel of the current frame. The segmentation mask is used for detecting objects, classifying objects and detecting events in the scene captured by the camera.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of detected objects, each of which has a signature representing features of the detected object. The match classifier matches object instances by analyzing data derived from the signatures. The match classifier determines whether the signatures match.

Abstract: Disclosed are systems and methods for reducing video communication bandwidth requirements of a video surveillance network camera system that includes network communication paths between network video cameras producing video streams of scenes observed by the network video cameras and content-aware computer networking devices analyzing by video analytics video visual content of the video streams to provide managed video representing, at specified quality levels, samples of the scenes observed. Distribution of the managed video consumes substantially less network bandwidth than would be consumed by delivery through network communication paths a video stream at the specified quality level in the absence of analysis by the video analytics.

Abstract: Video analytics is used to track an object of interest represented in video data representing the field of view of a scene observed by a video camera. A multidimensional virtual beam is used to detect whether the tracked object of interest is continually present in a detection zone within the field view of the scene. An occurrence of an event is signaled when the tracked object of interest is continually present in the detection zone during a period beginning when the tracked object of interest enters the detection zone and ending when the tracked object of interest leaves the detection zone through the opposite side, after having completely crossed through the detection zone. Use of a virtual beam detection zone reduces false alarms as compared to the numbers of incidences of false alarms of traditional detection methods, while adding several features and benefits.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A system and method for segmenting a current frame of a video includes receiving a segmentation mask determined from a previous frame of the video, receiving a background model value and a background deviation value for a given pixel of a previous frame of the video, updating the received background deviation value, updating the background model value independently of the updating of the background deviation value and determining a value of a pixel of a new segmentation mask corresponding to the given pixel based on a foreground model value of the current frame, a segmenting background model value of the current frame and a segmentation threshold, the updating defining whether the given pixel is a foreground pixel or a background pixel of the current frame. A chaotic noise mask may also be determined and weighting factors may be updated based on the noise ratio of the chaotic noise mask.

Abstract: An automated electronic video surveillance system enables a high-resolution mega-pixel camera to capture high quality, detailed, magnified images at multiple locations, simultaneously with an overview of the whole scene. A preferred embodiment requiring no moving parts provides full 360-degree continuous viewing with up to 5× all-digital zoom capability. The system performs continuous surveillance and active resolution allocation in the form of a feedback control subsystem that dynamically allocates resources so that important details within a scene receive appropriate scrutiny, while uninteresting areas are imaged at a lower resolution.

Abstract: A processor-implemented access control method includes receiving credential and policy directory information to configure an access controller to allow self-provisioning of the access controller through periodic, automated query of the directory by the access controller; acquiring from the directory, credential and policy information for one or more individuals who may require access; storing in a local cache the acquired credential and policy information; receiving an access request to allow an individual access; comparing the access request to the credential and policy information in the cache; and when the comparison indicates a match, granting the individual access.

Abstract: Video analytics is used to track an object of interest represented in video data representing the field of view of a scene observed by a video camera. A multidimensional virtual beam is used to detect whether the tracked object of interest is continually present in a detection zone within the field view of the scene. An occurrence of an event is signaled when the tracked object of interest is continually present in the detection zone during a period beginning when the tracked object of interest enters the detection zone and ending when the tracked object of interest leaves the detection zone through the opposite side, after having completely crossed through the detection zone. Use of a virtual beam detection zone reduces false alarms as compared to the numbers of incidences of false alarms of traditional detection methods, while adding several features and benefits.

Abstract: Video analytics is used to track an object of interest represented in video data representing the field of view of a scene observed by a video camera. A multidimensional virtual beam is used to detect whether the tracked object of interest is continually present in a detection zone within the field view of the scene. An occurrence of an event is signaled when the tracked object of interest is continually present in the detection zone during a period beginning when the tracked object of interest enters the detection zone and ending when the tracked object of interest leaves the detection zone through the opposite side, after having completely crossed through the detection zone. Use of a virtual beam detection zone reduces false alarms as compared to the numbers of incidences of false alarms of traditional detection methods, while adding several features and benefits.