Hewlett Packard Enterprise Development LP

Puneet Jain

Kyu Han Kim

Kyu-Han Kim

Theodore A Stone

Nathaniel Stone

Yurong Jiang

Xiaochen Liu

Soteris Demetriou

Animesh Srivastava

Examples include detecting objects and determining a set of features for the objects. Examples include receiving a first image input, generating a number of feature maps from the first image input using a number of convolution filters, generating a first number of fully connected layers directly based on the number of feature maps, and detecting a number of objects in the first image and determining a set of features for each object from the first number of fully connected layers.

Objects and features neural network

An example video analytics device can include a memory, a processor executing instructions stored in the memory, an active region detector to identify a plurality of active regions of a plurality of video streams, wherein the plurality of active regions are sections in the plurality of video streams that change from a first frame to a second frame, and a bin packer communicatively coupled with the processor to combine the plurality of active regions to produce a multi-batch of an individual slice, wherein the multi-batch of the individual slice is a batch including the plurality of active regions that is processed at once.

Video active region batching

An example system comprising: a processing resource; and a memory resource storing machine readable instructions executable to cause the processing resource to: receive a Bluetooth Low Energy (BLE) signal transmitted from a user device; generate, from the BLE signal, a BLE moving pattern of the user device, wherein the BLE moving pattern is generated at a different entity than an entity that transmits the BLE signal; track an object carrying the user device via visual information of the object such that a visual moving pattern of the object is generated from the tracking; determine the visual moving pattern matches the BLE moving pattern; and assign, responsive to the determination, an identity obtained from the user device to the object being tracked via the visual information.

Match bluetooth low energy (BLE) moving patterns

Examples herein relate to determining a next state in which to transition multiple IoT devices within an environment. Examples disclose determining, via operation of a state machine, a current state of the multiple IoT devices within the environment. The state machine receives contextual information. Based on the current state and the contextual information, the state machine determine a next state of the multiple IoT devices in which to transition of the multiple IoT devices within the environment.

Determination of a next state of multiple IoT devices within an environment

Examples disclosed herein relate to detecting camera access breaches by an application running on a computing device. The examples enable determining, by a computing device comprising a physical processor that implements machine readable instructions, that a type of camera access of a camera on a computing device is requested by an application running on the computing device, wherein the type of camera access comprises a photo, a video, a facial recognition, a bar code scanning, or object detection; determining, by the computing device and based on a set of camera access types associated with the application, whether the requested type of camera access is permitted; and responsive to determining that the requested type of camera access is not permitted, remediating the unpermitted camera access request by causing display, by the computing device, of an alert on the computing device, where the alert comprises information about an improper access of the camera by the application.

Detecting camera access breaches

Match Bluetooth low energy (BLE) moving patterns

A method is provided for real-time processing of IoT data. For example, a first physical processor of an edge computing device may receive a set of data from a first IoT device communicably coupled to the edge device. The first physical processor may split the set of data into a set of individual data packets. A second physical processor of the edge device process the set of individual data packets by: concurrently applying, by a plurality of instances of the second physical processor of the edge computing device, a learning model to each of a corresponding plurality of data packets from the set of individual data packets; and annotating, by a subset of the plurality of instances of the second physical processor, a corresponding subset of the plurality of data packets with a corresponding output from the concurrent application of the learning model.

Real-time processing of IoT data

Examples provided herein describe a method for determining car positions. For example, a physical processor of an edge computing device may receive position data for a legacy car and information about a make and model of the legacy car. The first edge device may also receive, from a sensor-rich car, a set of sensor data about a set of observed cars in the vicinity of the sensor-rich car, a set of position data for the set of observed cars, and a set of visual data of the set of observed cars, wherein the set of observed cars includes the legacy car and the sensor-rich car. The edge device may then determine an updated position for the legacy car based on the set of position data for the set of observed cars, the set of visual data, and the set of sensor data and provide the updated position of the legacy car.

Growing community of inventors

Palo Alto, CA, United States of America

Puneet Jain