Abstract: In various embodiments, software architecture and network topology are provided to implement a decentralized distributed computing environment. In some embodiments, the novel network topology is configured as a mesh network such that individual hub devices are connected directly or indirectly with each other. An individual worker can be added to or removed from this network via connection with the hub devices without affecting the rest of the workers in the network and without user intervention or knowledge.

Abstract: Various embodiments facilitate a user to program and control one or more devices through a control system. In some embodiments, an interface is provided to enable the user to manipulate one or more program elements graphically. The one or more program elements include a first program element corresponding to the task, and a user input is provided by the user through a user manipulation of the first program element in the interface. The user manipulation comprises drag and drop, voice control, gesture control and/or any other mode of control. In those embodiments, the user input is then converted a first code understandable to the control system. The first code is then transmitted to the control system through a communication protocol. After the first code is received, a first instruction is generated by the control system and is transmitted to an end device for execution by the first instruction.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.

Abstract: Embodiments described herein provide various examples of a face-image training data preparation system for performing large-scale face-image training data acquisition, preprocessing, cleaning, balancing, and post-processing. The disclosed training data preparation system can collect a very large set of loosely-labeled images of different people from the public domain, and then generate a raw training dataset including a set of incorrectly-labeled face images. The disclosed training data preparation system can then perform cleaning and balancing operations on the raw training dataset to generate a high-quality face-image training dataset free of the incorrectly-labeled face images. The processed high-quality face-image training dataset can be subsequently used to train deep-neural-network-based face recognition systems to achieve high performance in various face recognition applications.

Abstract: Embodiments described herein provide various examples of a face-detection system. In one aspect, a process for performing image detections on grayscale images is disclosed. This process can begin by receiving a training image dataset, wherein the training image dataset includes a first subset of color images. The process then converts each image in the first subset of color images in the training image dataset into a grayscale image to obtain a first subset of converted grayscale images. Next, the process trains an image-detection statistical model using the training image dataset including the first subset of converted grayscale images. The process next receives a set of grayscale input images. The process subsequently performs image detections on the set of grayscale input images using the trained image-detection statistical model.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.

Abstract: Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded vision system. In one aspect, a process for identifying near-duplicate-face images using this subsystem is disclosed. This process includes the steps of: receiving a determined best-pose-face image associated with a tracked face when the tracked face is determined to be lost; extracting an image feature from the best-pose-face image; computing a set of similarity values between the extracted image feature and each of a set of stored image features in a feature buffer, wherein the set of stored image features are extracted from a set of previously transmitted best-pose-face images; determining if any of the computed similarity values is above a predetermined threshold; and if no computed similarity value is above the predetermined threshold, transmitting the best-pose-face image to a server and storing the extracted image feature into the feature buffer.

Abstract: Embodiments described herein provide various examples of an age and gender estimation system capable of performing age and gender classifications on face images having sizes greater than the maximum number of input pixels supported by a given small-scale hardware convolutional neural network (CNN) module. In some embodiments, the proposed age and gender estimation system can first divide a high-resolution input face image into a set of image patches with judiciously designed overlaps among neighbouring patches. Each of the image patches can then be processed with a small-scale CNN module, such as the built-in CNN module in Hi3519 SoC. The outputs corresponding to the set of image patches can be subsequently merged to obtain the output corresponding to the input face image, and the merged output can be further processed by subsequent layers in the age and gender estimation system to generate age and gender classifications for the input face image.

Abstract: Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded video system. In one aspect, a process for performing real-time face-pose-estimation and best-pose selection for a detected person captured in a video is disclosed. This process includes the steps of: receiving a video image among a sequence of video frames of a video; performing a face detection operation on the video image to detect a set of faces in the video image; detecting a new person appears in the video based on the set of detected faces; tracking the new person through subsequent video images in the video by detecting a sequence of face images of the new person in the subsequent video images; and for each of the subsequent video images which contains a detected face of the new person being tracked: estimating a pose associated with the detected face and updating a best pose for the new person based on the estimated pose.

Abstract: Embodiments described herein provide various examples of a joint face-detection and head-pose-angle-estimation system based on using a small-scale hardware CNN module such as the built-in CNN module in HiSilicon Hi3519 system-on-chip. In some embodiments, the disclosed joint face-detection and head-pose-angle-estimation system is configured to jointly perform multiple tasks of detecting most or all faces in a sequence of video frames, generating pose-angle estimations for the detected faces, tracking detected faces of a same person across the sequence of video frames, and generating “best-pose” estimation for the person being tracked. The disclosed joint face-detection and pose-angle-estimation system can be implemented on resource-limited embedded systems such as smart camera systems that are only integrated with one or more small-scale CNN modules.

Abstract: Embodiments of a convolutional neural network (CNN) system based on using resolution-limited small-scale CNN modules are disclosed. In some embodiments, a CNN system includes: a receiving module for receiving an input image of a first image size, the receiving module can be used to partition the input image into a set of subimages of a second image size; a first processing stage that includes a first hardware CNN module configured with a maximum input image size, the first hardware CNN module is configured to sequentially receive the set of subimages and sequentially process the received subimages to generate a set of outputs; a merging module for merging the sets of outputs into a set of merged feature maps; and a second processing stage for receiving the set of feature maps and processing the set of feature maps to generate an output including at least one prediction on the input image.

Abstract: Embodiments described herein provide various examples of a face-image training data preparation system for performing large-scale face-image training data acquisition, pre-processing, cleaning, balancing, and post-processing. The disclosed training data preparation system can collect a very large set of loosely-labeled images of different people from the public domain, and then generate a raw training dataset including a set of incorrectly-labeled face images. The disclosed training data preparation system can then perform cleaning and balancing operations on the raw training dataset to generate a high-quality face-image training dataset free of the incorrectly-labeled face images. The processed high-quality face-image training dataset can be subsequently used to train deep-neural-network-based face recognition systems to achieve high performance in various face recognition applications.

Abstract: Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded vision system. In one aspect, a process for identifying near-duplicate-face images using this subsystem is disclosed. This process includes the steps of: receiving a determined best-pose-face image associated with a tracked face when the tracked face is determined to be lost; extracting an image feature from the best-pose-face image; computing a set of similarity values between the extracted image feature and each of a set of stored image features in a feature buffer, wherein the set of stored image features are extracted from a set of previously transmitted best-pose-face images; determining if any of the computed similarity values is above a predetermined threshold; and if no computed similarity value is above the predetermined threshold, transmitting the best-pose-face image to a server and storing the extracted image feature into the feature buffer.

Abstract: Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded video system. In one aspect, a process for performing real-time face-pose-estimation and best-pose selection for a detected person captured in a video is disclosed. This process includes the steps of: receiving a video image among a sequence of video frames of a video; performing a face detection operation on the video image to detect a set of faces in the video image; detecting a new person appears in the video based on the set of detected faces; tracking the new person through subsequent video images in the video by detecting a sequence of face images of the new person in the subsequent video images; and for each of the subsequent video images which contains a detected face of the new person being tracked: estimating a pose associated with the detected face and updating a best pose for the new person based on the estimated pose.

Abstract: Embodiments described herein provide various examples of a face detection system, based on using a small-scale hardware convolutional neural network (CNN) module configured into a multi-task cascaded CNN. In some embodiments, a subimage-based CNN system can be configured to be equivalent to a large-scale CNN that processes the entire input image without partitioning such that the output of the subimage-based CNN system can be exactly identical to the output of the large-scale CNN. Based on this observation, some embodiments of this patent disclosure make use of the subimage-based CNN system and technique on one or more stages of a cascaded CNN or a multitask cascaded CNN (MTCNN) so that a larger input image to a given stage of the cascaded CNN or the MTCNN can be partitioned into a set of subimages of a smaller size. As a result, each stage of the cascaded CNN or the MTCNN can use the same small-scale hardware CNN module that is associated with a maximum input image size constraint.

Abstract: Embodiments described herein provide various examples of a joint face-detection and head-pose-angle-estimation system based on using a small-scale hardware CNN module such as the built-in CNN module in HiSilicon Hi3519 system-on-chip. In some embodiments, the disclosed joint face-detection and head-pose-angle-estimation system is configured to jointly perform multiple tasks of detecting most or all faces in a sequence of video frames, generating pose-angle estimations for the detected faces, tracking detected faces of a same person across the sequence of video frames, and generating “best-pose” estimation for the person being tracked. The disclosed joint face-detection and pose-angle-estimation system can be implemented on resource-limited embedded systems such as smart camera systems that are only integrated with one or more small-scale CNN modules.

Abstract: Embodiments described herein provide various examples of a face detection system, based on using a small-scale hardware convolutional neutral network (CNN) module configured into a multi-task cascaded CNN. In some embodiments, a subimage-based CNN system can be configured to be equivalent to a large-scale CNN that processes the entire input image without partitioning such that the output of the subimage-based CNN system can be exactly identical to the output of the large-scale CNN. Based on this observation, some embodiments of this patent disclosure make use of the subimage-based CNN system and technique on one or more stages of a cascaded CNN or a multitask cascaded CNN (MTCNN) so that a larger input image to a given stage of the cascaded CNN or the MTCNN can be partitioned into a set of subimages of a smaller size. As a result, each stage of the cascaded CNN or the MTCNN can use the same small-scale hardware CNN module that is associated with a maximum input image size constraint.

Abstract: Embodiments of a convolutional neutral network (CNN) system based on using resolution-limited small-scale CNN modules are disclosed. In some embodiments, a CNN system includes: a receiving module for receiving an input image of a first image size, the receiving module can be used to partition the input image into a set of subimages of a second image size; a first processing stage that includes a first hardware CNN module configured with a maximum input image size, the first hardware CNN module is configured to sequentially receive the set of subimages and sequentially process the received subimages to generate a set of outputs; a merging module for merging the sets of outputs into a set of merged feature maps; and a second processing stage for receiving the set of feature maps and processing the set of feature maps to generate an output including at least one prediction on the input image.

Abstract: Embodiments described herein provide various examples of an age and gender estimation system capable of performing age and gender classifications on face images having sizes greater than the maximum number of input pixels supported by a given small-scale hardware convolutional neutral network (CNN) module. In some embodiments, the proposed age and gender estimation system can first divide a high-resolution input face image into a set of image patches with judiciously designed overlaps among neighbouring patches. Each of the image patches can then be processed with a small-scale CNN module, such as the built-in CNN module in Hi3519 SoC. The outputs corresponding to the set of image patches can be subsequently merged to obtain the output corresponding to the input face image, and the merged output can be further processed by subsequent layers in the age and gender estimation system to generate age and gender classifications for the input face image.