Abstract: Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

Abstract: Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

Abstract: Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

Abstract: Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

Abstract: A method for image ghost removal is provided. The method for image ghost removal includes receiving an image and a background model related to the image, and generating a foreground mask based on the image and the background model. The method also includes identifying image ghosts within the foreground mask, and updating the background model to eliminate the image ghosts.

Abstract: A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.

Abstract: A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.

Abstract: A method for extracting line segments from an edge image comprises receiving a digital image comprising a plurality of edge pixels, and processing the plurality of edge pixels using a breadth first search to determine a plurality of breadth first search pixels in a breadth first search order for a connected component. The connected component comprises a plurality of components. The method continues by processing the plurality of breadth first search pixels in an order related to the breadth first search order to determine a plurality of component pixels for at least one component of the plurality of components. Each of the plurality of components comprises a line segment. The method concludes by processing the plurality of component pixels to determine a plurality of line segment pixels for the line segment.

Abstract: A video system for determining a region of interest in a video comprising a video source and a video processing system is provided. The video processing system is configured to receive the video from the video source and identify at least one object in the video which is in contact with the floor. The video processing system is further configured to determine a contact point between the object and the floor in a frame of the video and identify at least one polygon representing the floor in the frame, wherein the polygon includes the contact point. The video processing system is further configured to identify a three dimensional volume representing a space above the polygon extending to a designated height and select the region of interest by identifying a two dimensional area of the frame based on the three dimensional volume.

Abstract: A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.

Abstract: A method for analyzing queues using video analytics is provided. The method includes receiving a video comprising a plurality of images of a scene. The scene includes a queue region and an operation region. The method also includes processing at least a first image of the plurality of images to determine an occurrence of a first event associated with the operation region, and processing at least a second image of the plurality of images to determine an occurrence of a second event associated with the operation region. The method further includes determining an operation time based on an amount of time between the first event and the second event, processing at least a third image of the plurality of images to determine a quantity of entities in the queue region, and determining a wait time based on the operation time and the quantity of entities in the queue region.

Abstract: A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.

Abstract: A method for extracting line segments from an edge image comprises receiving a digital image comprising a plurality of edge pixels, and processing the plurality of edge pixels using a breadth first search to determine a plurality of breadth first search pixels in a breadth first search order for a connected component. The connected component comprises a plurality of components. The method continues by processing the plurality of breadth first search pixels in an order related to the breadth first search order to determine a plurality of component pixels for at least one component of the plurality of components. Each of the plurality of components comprises a line segment. The method concludes by processing the plurality of component pixels to determine a plurality of line segment pixels for the line segment.

Abstract: A method for extracting line segments from an edge image comprises receiving a digital image comprising a plurality of edge pixels, and processing the plurality of edge pixels using a breadth first search to determine a plurality of breadth first search pixels in a breadth first search order for a connected component. The connected component comprises a plurality of components. The method continues by processing the plurality of breadth first search pixels in an order related to the breadth first search order to determine a plurality of component pixels for at least one component of the plurality of components. Each of the plurality of components comprises a line segment. The method concludes by processing the plurality of component pixels to determine a plurality of line segment pixels for the line segment.