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 multi-dimensional 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, 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 a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

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 a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

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 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 an object classification module connected to the image capturing device. The image capturing device has a field of view and produces image data representing an image of the field of view. The object classification module is operable to determine whether an object in an image is a member of an object class. The object classification module includes N decision steps configured in a cascade configuration, wherein at least one of the N 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 an object is a member of the object class.

Abstract: Video analytics and a mass storage unit are contained in a camera housing of a video camera. The video analytics analyzes video data produced by the video camera and detects whether there is an occurrence of a defined event of interest. The video data representing the field of view of the scene observed by the video camera are stored in the mass storage unit. The stored video data include video data of first and second qualities. The first quality represents the occurrence in a field of view of the video camera of the defined event of interest detected by the video analytics. The second quality represents the nonoccurrence in the field of view of the defined event of interest detected by the video analytics. Storing video data of first and second qualities reduces system storage capacity demands.

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 a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

Abstract: Video analytics and a mass storage unit are contained in a camera housing of a video camera. The camera produces video data representing the field of view of a scene observed by the camera. The video analytics analyzes the video data and detects whether there is an occurrence of a defined feature of an object or a defined event of interest. The video analytics also generates metadata based upon the content of the video data. The video data and metadata are stored in the mass storage unit. Access to stored video data may be controlled based on content, and access to live video data may be controlled as metadata corresponding to the video data is created. The metadata can be used to search for corresponding video data representing the detected feature of an object or event of interest observed in the scene.

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 multi-dimensional 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: 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: 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 a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

Abstract: A storage buffer and a hard-disk drive are provided in a video data storage application. The hard-disk drive operates normally in a powered-down state, and video data are stored in the storage buffer during a first time interval. The hard-disk drive is operated in a powered-up state. A portion of the video data stored in the storage buffer is transferred to and stored in the hard-disk drive in the powered-up state during a second time interval that is substantially shorter than the first time interval. The hard-disk drive is returned to the powered-down state. A total hard-disk drive storage time, representing a sum of the second time intervals, spent transferring to and storing a portion of the video data in the hard-disk drive is substantially less than a total buffer storage time, representing a sum of the first time intervals, spent storing the video data in the storage buffer.

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 a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

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 a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

Abstract: A video system for intelligent visual display includes a video camera having an imager that produces a first set of image data representing an image of a scene. Video analytics receive and analyze the first set of image data to track an object. The video analytics generate metadata synchronized to the first set of image data and representing location information of an image of the object in the image of the scene. A display management module receives the first set of image data and the metadata and uses the metadata to derive from the first set of image data a second set of image data representing a cropped close-up image of the object. The second set of image data is distinct from the first set of image data to enable separate display presentation of the cropped close-up image and the image of the scene.

Abstract: Methods and systems detect objects of interest in a spatio-temporal signal. According to one embodiment, a system processes a digital spatio-temporal input signal containing zero or more foreground objects of interest superimposed on a background. The system comprises a foreground/background separation module, a foreground object grouping module, an object classification module, and a feedback connection. The foreground/background separation module receives the spatio-temporal input signal and, according to one or more adaptable parameters, produces foreground/background labels designating elements of the spatio-temporal input signal as either foreground or background. The foreground object grouping module is connected to the foreground/background separation module and identifies groups of selected foreground-labeled elements as foreground objects. The object classification module is connected to the foreground object grouping module and generates object-level information related to the foreground object.

Abstract: Video analytics and a mass storage unit are contained in a camera housing of a video camera. The camera produces video data representing the field of view of a scene observed by the camera. The video analytics analyzes the video data and detects whether there is an occurrence of a defined feature of an object or a defined event of interest. The video analytics also generates metadata based upon the content of the video data. The video data and metadata are stored in the mass storage unit. Access to stored video data may be controlled based on content, and access to live video data may be controlled as metadata corresponding to the video data is created. The metadata can be used to search for corresponding video data representing the detected feature of an object or event of interest observed in the scene.

Abstract: Video analytics and a mass storage unit are contained in a camera housing of a video camera. Video data representing a field of view of a scene observed by the camera are stored in the mass storage unit. The video analytics analyzes video data produced by the camera and detects whether there is an occurrence of a defined event of interest. A video clip of the scene representing the event of interest is sent to a remote storage unit. Since only about 1% of security video data is reviewed, storing only video data representing events of interest remotely, while storing more complete video data of the scene observed by the camera local to the camera, reduces the remote storage capacity and bandwidth demand for the video system. Remote redundant storage of events of interest also provides higher reliability and fault tolerant storage for the video data that are most important.