Abstract: One example temperature sensing device includes an electronic processor configured to receive a thermal image of a person captured by a thermal camera. The electronic processor is configured to determine a first temperature and a first location of a first hotspot on the person. The electronic processor is configured to determine a second location of a second hotspot on the person based on the second location being approximately symmetrical with respect to the first location about an axis, and the second hotspot having a second temperature that is approximately equal to the first temperature. The electronic processor is configured to determine a distance between the first location of the first hotspot and the second location of the second hotspot. In response to determining that the distance is within the predetermined range of distances, the electronic processor is configured to generate and output an estimated temperature of the person.

Abstract: Methods, systems, and techniques for tagless tracking of an object-of-interest are disclosed. Image and non-image data are generated across a plurality of camera-specific regions, and the object-of-interest is tracked over a global map formed as a composite of these regions.

Abstract: Methods, systems, and techniques for enhancing use of two-dimensional (2D) video analytics by using depth data. Two-dimensional image data representing an image comprising a first object is obtained, as well as depth data of a portion of the image that includes the first object. The depth data indicates a depth of the first object. An initial 2D classification of the portion of the image is generated using the 2D image data without using the depth data. The initial 2D classification is stored as an approved 2D classification when the initial 2D classification is determined consistent with the depth data. Additionally or alternatively, a confidence level of the initial 2D classification may be adjusted depending on whether the initial 2D classification is determined to be consistent with the depth data, or the depth data may be used with the 2D image data for classification.

Abstract: Methods, systems, and techniques for monitoring an object-of-interest within a region involve receiving at least data from two sources monitoring a region and correlating that data to determine that an object-of-interest depicted or represented in data from one of the sources is the same object-of-interest depicted or represented in data from the other source. Metadata identifying that the object-of-interest from the two sources is the same object-of-interest is then stored for later use in, for example, object tracking.

Abstract: One example temperature sensing device includes an electronic processor configured to receive a thermal image of a person captured by a thermal camera. The electronic processor is configured to determine a first temperature and a first location of a first hotspot on the person. The electronic processor is configured to determine a second location of a second hotspot on the person based on the second location being approximately symmetrical with respect to the first location about an axis, and the second hotspot having a second temperature that is approximately equal to the first temperature. The electronic processor is configured to determine a distance between the first location of the first hotspot and the second location of the second hotspot. In response to determining that the distance is within the predetermined range of distances, the electronic processor is configured to generate and output an estimated temperature of the person.

Abstract: A device, method and system for adjusting a configuration of a camera device is provided. An example device is in communication with an example camera device having a configuration. The device receives an image sequence from the camera device. The device processes the image sequence to determine a predicted location of a target object identified in the image sequence based on movement of the target object in the image sequence. The device adjusts the configuration of the camera device based on a stored configuration of the camera device, the stored configuration indicative of conditions at the predicted location.

Abstract: Systems for personalized intent prediction may perform a process including receiving image data depicting the behavior of a person on approach toward a secure location, generating intent data including data representing a current trajectory of the person on the current approach, comparing the intent data with a personal statistical model for the person that includes data representing trajectories associated with historical approaches by the person toward the secure location and respective result data indicating whether the historical approach resulted in the person entering the secure location, determining, dependent on the comparing, a personalized intent score representing a likelihood that the person intends to enter the secure location on the current approach, and pre-emptively enabling, or refraining from pre-emptively enabling, entry to the secure location prior to the person reaching the secure location based on whether the personalized intent score meets or exceeds a predetermined minimum confidence t

Abstract: A smart badge, and a method, system and computer program product for badge detection and compliance are disclosed. The method, carried out within a security system, includes capturing, using a camera, an image of a person. The captured image includes a face of the person within a first pixel region of the image. The method also includes performing facial recognition on the first pixel region to determine an identity of the person. The method also includes performing video analytics, on a second pixel region of the image, different than the first pixel region, to make a first determination that the identified person is wearing a badge, or to make a second determination that no badge is being properly worn by the identified person. The method also includes generating an alert, specific to the identified person, within the security system based at least in part on the first or second determination.

Abstract: Systems for personalized intent prediction may perform a process including receiving image data depicting the behavior of a person on approach toward a secure location, generating intent data including data representing a current trajectory of the person on the current approach, comparing the intent data with a personal statistical model for the person that includes data representing trajectories associated with historical approaches by the person toward the secure location and respective result data indicating whether the historical approach resulted in the person entering the secure location, determining, dependent on the comparing, a personalized intent score representing a likelihood that the person intends to enter the secure location on the current approach, and pre-emptively enabling, or refraining from pre-emptively enabling, entry to the secure location prior to the person reaching the secure location based on whether the personalized intent score meets or exceeds a predetermined minimum confidence t

Abstract: One embodiment provides an access control system including access control sensors to detect actions performed in a vicinity of an access point, a verification sensor to verify access of the access point, and an electronic processor communicatively coupled to the access control sensors and the verification sensor. The electronic processor is configured to in response to an access intent model satisfying an accuracy condition, deploy the access intent model for the access point and receive a dataset indicating an action performed in the vicinity of the access point. The electronic processor is also configured to predict an access intent to access the access point by applying the access intent model to the dataset and enable access through the access point. The electronic processor is further configured to receive verification data indicating whether the access point is accessed, and automatically assign a label to the dataset based on the verification data.

Abstract: Alias capture to support searching for an object-of-interest is disclosed. A method includes capturing, using a camera with a defined field of view, video image frames that include a moving object-of-interest. The method also includes tracking the object-of-interest over a period of time starting when the object-of-interest enters the field of view and ending when the object-of-interest exits the field of view. The method also includes detecting, at a point in time in-between the start and end of the period of time of the tracking, a threshold exceeding change in an appearance of the object-of-interest. The method also includes creating, before the end of the period of time of the tracking, a new object profile for the object-of-interest in response to the detecting of the threshold exceeding change.

Abstract: Methods, systems, and techniques for classifying and/or detecting objects using visible and invisible light images. A visible light image and an invisible light image are received at a convolutional neural network (CNN). The visible light image depicts a region-of-interest imaged using visible light. The invisible light image depicts at least a portion of the region-of-interest imaged using invisible light, and at least one of the images depicts an object-of-interest within the portion of the region-of-interest shared between the images. The CNN then classifies and/or detects the object-of-interest using the images. The CNN may be trained to perform this classification and/or detection using pairs of visible and invisible light training images.

Abstract: Systems for detecting liveness in image data may perform a process including receiving, from one or more image capture devices, two-dimensional image data representing two images captured simultaneously and depicting the same human person, including data representing light captured in two spectral bands, such as visible light and infrared light. The received image data may be converted to grayscale or downsampled prior to further processing. The process may include creating a two-dimensional combined image representation of the received image data and analyzing the combined image representation to detect any implied three-dimensional features using a neural network or machine learning. The process may include classifying the received image data as likely to depict a live person or a two-dimensional spoof of a live person, dependent on the analysis, and outputting a classification result indicating a likelihood that the received image data depicts a live person.

Abstract: A computer-implemented method comprises detecting, with a presence detector such as a radar device, a presence of a person at a location such as an ATM vestibule. In response thereto, a timer is initiated. After initiating the timer, a portal sensor is used to detect a change in a status of a portal permitting access to the location. In response thereto, the timer is adjusted (for example the timer is reset). Thus, multiple consecutive normal usages of the vestibule do not unnecessarily trigger an alarm.

Abstract: Automated processing, enforcement and intelligent management of vehicle operation violations is disclosed. A method, that is also disclosed, includes obtaining at least one image within which is shown at least a portion of a vehicle. The method also includes receiving image data for the at least one image, and analyzing the image data to generate a violation score for each of one or more potential vehicle operation violations for the vehicle. When the violation score is in-between an upper violation threshold and a lower consideration threshold, user input is obtained that either affirms or rejects existence of a violation and then, once the user input is obtained, a violation notification is generated only when the user input affirms the existence of the violation. When the violation score is higher than the violation threshold, the violation notification is generated without the user input being obtained.

Abstract: A device, system and method for controlling a passage barrier mechanism is provided. While failing to detect, via a biometric imaging system in communication with a passage barrier mechanism, unauthorized users in a first area of interest of the biometric imaging system, a device: maintains the passage barrier mechanism in a multiple entry state in which multiple users at a time authenticate, via the biometric imaging system, and enter therethrough. Responsive to detecting, via the biometric imaging system, an unauthorized user in the first area of interest, the device: transitions the passage barrier mechanism from the multiple entry state to a single entry state in which only a single user at a time authenticates, via the biometric imaging system, and enters therethrough; and changes a biometric authentication range of the biometric imaging system from the first area of interest to a smaller second area of interest.

Abstract: A system is provided, including: a radar sensor configured to transmit and receive a radar signal from a person; a depth camera configured to receive a depth image of the person; one or more processors communicative with memory having stored thereon computer program code configured when executed by the one or more processors to cause the one or more processors to perform a method comprising: detect the person; determine depth information relating to the person using the depth image; determine a correlation between the depth information of the person and the radar signal received from the person; and in response to the correlation not within a range of expected values, generating an alert. The depth information may be a volume or surface area of the person.

Abstract: Alias capture to support searching for an object-of-interest is disclosed. A method includes capturing, using a camera with a defined field of view, video image frames that include a moving object-of-interest. The method also includes tracking the object-of-interest over a period of time starting when the object-of-interest enters the field of view and ending when the object-of-interest exits the field of view. The method also includes detecting, at a point in time in-between the start and end of the period of time of the tracking, a threshold exceeding change in an appearance of the object-of-interest. The method also includes creating, before the end of the period of time of the tracking, a new object profile for the object-of-interest in response to the detecting of the threshold exceeding change.

Abstract: Methods, systems, and techniques for generating two-dimensional (2D) and three-dimensional (3D) images and image streams. The images and image streams may be generated using active stereo cameras projecting at least one illumination pattern, or by using a structured light camera and a pair of different illumination patterns of which at least one is a structured light illumination pattern. When using an active stereo camera, a 3D image may be generated by performing a stereoscopic combination of a first set of images (depicting a first illumination pattern) and a 2D image may be generated using a second set of images (optionally depicting a second illumination pattern). When using a structured light camera, a 3D image may be generated based on a first image that depicts a structured light illumination pattern, and a 2D image may be generated from the first image and a second image that depicts a different illumination pattern.

Abstract: A method, system and computer program product for emulating depth data of a three-dimensional camera device is disclosed. The method includes concurrently operating the radar device and the 3D camera device to generate training radar data and training depth data respectively. Each of the radar device and the 3D camera device has a respective field of view. The field of view of the radar device overlaps the field of view of the 3D camera device. The method also includes inputting the training radar and depth data to the neural network. The method also includes employing the training radar and depth data to train the neural network. Once trained, the neural network is configured to receive real radar data as input and to output substitute depth data.