Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first class of a first object depicted in an image frame from a classification model and subsequently receiving a second image frame. The implementation further includes predicting, using a classification tracking model, that the classification model will output the first class for the second image frame and then detecting whether the first class is in fact outputted. The implementation includes determining that the second image frame should be added to a training dataset for the classification model when detecting that the classification model did not generate the first class for the second image frame as predicted and re-training the classification model using the training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: A method for counting number of people in an image includes feeding an image into an object counting model to generate a first result for the image that is indicative of a number of human heads detected in one or more regions of the image. The image is fed into a regression model to generate a second result for the image that is indicative of a regression count of people detected in the one or more regions of the image. An estimate of people detected in the image is obtained based on the first result and the second result using one or more rules.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a visual code identifier associated with a transaction of one or more transaction merchandises, receiving a radio frequency identification (RFID) identifier associated with the transaction, analyzing the image, determining at least one of one or more detected merchandises, a number of the one or more detected merchandises, and a price of the one or more detected merchandises based on information in the RFID identifier and the analyzed image, determining a merchandise discrepancy, a number discrepancy, or a price discrepancy, and transmitting, in response to determining the at least one of the merchandise discrepancy, the number discrepancy, or the price discrepancy, an alert.

Abstract: Apparatus and methods for detecting an object in an image. One aspect of the method includes receiving a first image from an image sensor. The first image is obtained by the image sensor using a first focal length. A second image is received from the image sensor. The second image is obtained by the image sensor using a second focal length. One or more objects are detected in the first image and the second image. The one or more objects detected in the first image are combined with the one or more objects detected in the second image.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a visual code identifier associated with a transaction of one or more transaction merchandises, receiving a radio frequency identification (RFID) identifier associated with the transaction, analyzing the image, determining at least one of one or more detected merchandises, a number of the one or more detected merchandises, and a price of the one or more detected merchandises based on information in the RFID identifier and the analyzed image, determining a merchandise discrepancy, a number discrepancy, or a price discrepancy, and transmitting, in response to determining the at least one of the merchandise discrepancy, the number discrepancy, or the price discrepancy, an alert.

Abstract: A system and method for identification and suppression of time varying background objects is disclosed. A proposed surveillance security system includes an image analytics system and at least one surveillance camera. The image analytics system identifies objects within image data of a scene captured by the surveillance camera, and then analyzes the objects to determine whether each object is a foreground object or a dynamic background object. In examples, the image analytics system determines whether an object is a foreground object or a dynamic background object based upon movement and/or an appearance of the object. The surveillance security system does not send alerts to users of the system for objects determined to be dynamic background objects. When users request objects of interest, the dynamic background objects are also excluded from lists of objects sent in reply messages to the users.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.

Abstract: A surveillance camera system and method is disclosed. The system includes one or more surveillance cameras that capture images of scenes, and one or more calibration systems that automatically generate ground planes from the captured images from the surveillance cameras. Foreground objects in the scenes are then analyzed against the ground planes to determine whether the ground planes may require updating and/or recalculation.

Abstract: A system and method for identification and suppression of time varying background objects is disclosed. A proposed surveillance security system includes an image analytics system and at least one surveillance camera. The image analytics system identifies objects within image data of a scene captured by the surveillance camera, and then analyzes the objects to determine whether each object is a foreground object or a dynamic background object. In examples, the image analytics system determines whether an object is a foreground object or a dynamic background object based upon movement and/or an appearance of the object. The surveillance security system does not send alerts to users of the system for objects determined to be dynamic background objects. When users request objects of interest, the dynamic background objects are also excluded from lists of objects sent in reply messages to the users.

Abstract: Techniques are disclosed for visually conveying a percept. The percept may represent information learned by a video surveillance system. A request may be received to view a percept for a specified scene. The percept may have been derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. A visual representation of the percept may be generated. A user interface may be configured to display the visual representation of the percept and to allow a user to view and/or modify metadata attributes with the percept. For example, the user may label a percept and set events matching the percept to always (or never) result in alert being generated for users of the video surveillance system.

Abstract: A system and method for identification and suppression of time varying background objects is disclosed. A proposed surveillance security system includes an image analytics system and at least one surveillance camera. The image analytics system identifies objects within image data of a scene captured by the surveillance camera, and then analyzes the objects to determine whether each object is a foreground object or a dynamic background object. In examples, the image analytics system determines whether an object is a foreground object or a dynamic background object based upon movement and/or an appearance of the object. The surveillance security system does not send alerts to users of the system for objects determined to be dynamic background objects. When users request objects of interest, the dynamic background objects are also excluded from lists of objects sent in reply messages to the users.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.

Abstract: A system and method for identification and suppression of time varying background objects is disclosed. A proposed surveillance security system includes an image analytics system and at least one surveillance camera. The image analytics system identifies objects within image data of a scene captured by the surveillance camera, and then analyzes the objects to determine whether each object is a foreground object or a dynamic background object. In examples, the image analytics system determines whether an object is a foreground object or a dynamic background object based upon movement and/or an appearance of the object. The surveillance security system does not send alerts to users of the system for objects determined to be dynamic background objects. When users request objects of interest, the dynamic background objects are also excluded from lists of objects sent in reply messages to the users.

Abstract: A surveillance camera system and method is disclosed. The system includes one or more surveillance cameras that capture images of scenes, and one or more calibration systems that automatically generate ground planes from the captured images from the surveillance cameras. Foreground objects in the scenes are then analyzed against the ground planes to determine whether the ground planes may require updating and/or recalculation.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.