Abstract: Introduced here is a training system that allows users to define what they would like their surveillance systems to detect, recognize, or otherwise identify. For simplicity, the use case provided by a given user for teaching purposes may be referred to as a “skill.” A surveillance system can learn each of the skills provided by its respective user from a limited number of examples. For example, the given user may only provide several examples—or even a single example—in contrast to conventional learning approaches.

Abstract: Introduced here are different variations of an edge-cloud collaboration framework (also called an “ECC framework”) that learns models with different levels of tradeoffs between the aforementioned objectives that tend to conflict with one another. This ECC framework—based on an adaptation of knowledge from “edge models” employed by edge devices to “cloud models” employed by a computer server system—can attempt to minimize the communication and computation costs during the inference stage while also trying to achieve the best performance possible.

Abstract: A system and method for improving the ability of a camera to detect objects or events occurring within its field of view and to accurately categorize them using Artificial Intelligence (AI) aided by input from users. The camera may include rules for determining when an object has entered its field of view, and for determining what category of object it is. When a new object is detected, an alert may be sent to a user and optionally to an analytics service as well. The user may provide input confirming whether the category of the event was correctly determined, and the analytics service may apply an AI algorithm to determine what, if any, changes should be made to the rule criteria in the camera. Updated rule criteria may be sent back to the camera thus improving its ability to detect objects in the future.

Abstract: Introduced here is an approach to training a model to perform facial recognition with a designed dataset-aware loss. This approach is particularly useful for large-scale, multi-dataset training since there are no prerequisites for label cleaning. Dataset-aware loss may be built upon the softmax function with a binary dataset indicator. Such an approach allows the dataset-aware loss to be readily combined with other losses to improve performance.

Abstract: This technology relates to optimizing location and orientation information of an image using known locations of places captured within the image. For example, an image and associated pose data including the image's orientation and location may be received. One or more places captured within the image may be determined, with each place having a respective known location. The image may be annotated with the one or more places. A difference between each annotation and its respective known location to obtain updated pose data of the image may be minimized and the associated pose data may be updated to the updated pose data.

Abstract: An image processing system for recognizing the scene type of an input image generates an image distance metric from a set of images. The image processing system further extracts image features from the input image and each image in the set of images. Based on the distance metric and the extracted image features, the image processing system computes image feature distances for selecting a subset of images. The image processing system derives a scene type from the scene type of the subset of images. In one embodiment, the image processing system is a cloud computing system.

Abstract: This technology relates to optimizing location and orientation information of an image using known locations of places captured within the image. For example, an image and associated pose data including the image's orientation and location may be received. One or more places captured within the image may be determined, with each place having a respective known location. The image may be annotated with the one or more places. A difference between each annotation and its respective known location to obtain updated pose data of the image may be minimized and the associated pose data may be updated to the updated pose data.

Abstract: An image processing system for recognizing the scene type of an input image generates an image distance metric from a set of images. The image processing system further extracts image features from the input image and each image in the set of images. Based on the distance metric and the extracted image features, the image processing system computes image feature distances for selecting a subset of images. The image processing system derives a scene type from the scene type of the subset of images. In one embodiment, the image processing system is a cloud computing system.

Abstract: An image processing system for recognizing the scene type of an input image generates an image distance metric from a set of images. The image processing system further extracts image features from the input image and each image in the set of images. Based on the distance metric and the extracted image features, the image processing system computes image feature distances for selecting a subset of images. The image processing system derives a scene type from the scene type of the subset of images. In one embodiment, the image processing system is a cloud computing system.

Abstract: A method for automatically generating photo albums is performed by a server computer. The server computer retrieves a set of images over the network interface before it retrieves metadata and a first set of tags from each image. The server computer also generates a second set of tags for each image, and stores the sets of tags into a database. The server computer further determines a set of search parameters and retrieves a second set of images from the database. A photo album is then generated from the second set of images.

Abstract: A method for recognizing a face in an image is performed by a facial recognition system. The system retrieves an image and detects a face within the image. The system then determines a set of facial feature positions for a set of facial features. The set of facial feature positions are used to separate the face into a set of facial feature parts. For each part, the system extracts a set of image features. The extracted features are concatenated into a full feature. The system performs dimension reduction on the full feature to derive a final feature. In addition, although narrow claims may be presented below, it should be recognized that the scope of this invention is much broader than presented by the claim(s). It is intended that broader claims will be submitted in one or more applications that claim the benefit of priority from this application.

Abstract: A method for recognizing a face in an image is performed by a facial recognition system. The system retrieves an image and detects a face within the image. The system then determines a set of facial feature positions for a set of facial features. The set of facial feature positions are used to separate the face into a set of facial feature parts. For each part, the system extracts a set of image features. The extracted features are concatenated into a full feature. The system performs dimension reduction on the full feature to derive a final feature.

Abstract: An image processing system for recognizing the scene type of an input image generates an image distance metric from a set of images. The image processing system further extracts image features from the input image and each image in the set of images. Based on the distance metric and the extracted image features, the image processing system computes image feature distances for selecting a subset of images. The image processing system derives a scene type from the scene type of the subset of images. In one embodiment, the image processing system is a cloud computing system.

Abstract: An image processing system for recognizing the scene type of an input image generates an image distance metric from a set of images. The image processing system further extracts image features from the input image and each image in the set of images. Based on the distance metric and the extracted image features, the image processing system computes image feature distances for selecting a subset of images. The image processing system derives a scene type from the scene type of the subset of images. In one embodiment, the image processing system is a cloud computing system.