Western Digital Technologies, Inc.

Haoyu Wu

Toshiki Hirano

Shaomin Xiong

David Berman

Hussam Zebian

Jeff Wilke

Qian Zhong

Timothy Skarzynski

A surveillance system includes at least one image or video capture device and a controller configured to determine a change in location for the at least one image or video capture device from a first location to a second location. First image or video data is received from the at least one image or video capture device at the second location, and in response to the location change, a preconfigured neural network is obtained or weights for a neural network are obtained based at least in part on the received first image or video data. Second image or video data is received from the at least one image or video capture device at the second location and an inference operation is performed on the second image or video data by processing the second image or video data using the obtained weights or the obtained preconfigured neural network.

Smart video surveillance system using a neural network engine

In one embodiment, a sensor device includes a sensor configured to obtain sensor data and a communication interface configured to communicate with a set of secondary sensor devices. The set of secondary sensor devices comprise a set of secondary sensors and a set of secondary processing devices. The set of secondary sensors obtain additional sensor data. The set of secondary processing devices process the additional sensor data based on a set of secondary machine learning models, and generate activation data based on the additional sensor data. The sensor device also includes a processing device. The processing device is also configured to receive the activation data from the set of secondary sensor devices and cause the sensor to obtain first sensor data based on the activation data. The processing device is further configured to generate one or more inferences based on the first sensor data and a machine learning model.

Sensor system with low power sensor devices and high power sensor devices

This disclosure relates to an apparatus for object detection. The apparatus comprises a video camera, an object detector, and a controller. The video camera may be configured to generate a video stream of frames. The object detector may be configured to accept the video stream as input data and to perform object detection. The controller may be coupled to the video camera and the object detector. The controller may be configured to manage object detection in order to satisfy a performance metric and/or operate within an operational constraint.

Object detection using multiple neural network configurations

Systems and methods for surveillance video streams with embedded object data from another video camera are described. At least two video cameras are configured with fields of view to provide images of an object from alternative views. Video data for a primary video stream is received from one camera and secondary object data for the object from the other camera is embedded in the primary video stream. The primary video stream is sent to an analytics engine for processing the primary video and embedded secondary object data, such as performing facial recognition on a better image of a human face and/or feature vectors therefrom that are embedded in the primary video stream.

Surveillance video streams with embedded object data

A data storage system may include multiple data storage devices, such as solid-state drives, an enclosure housing the devices, and a thermal bridge positioned in a gap between and in contact with each of the enclosure and a device, where the enclosure is cooler than the device. Thus, heat is conducted away from a hot spot of the device and to the enclosure. The thermal bridge may be flexible enough to bridge different sized gaps, while stiff enough to generate contact forces applied to the enclosure and the device. For example, the thermal bridge may be constructed primarily of copper and configured to function like a compression spring.

Hot spot cooling for data storage system

Remote neural network retraining for surveillance systems is disclosed. In some cases, systems and methods enable remote training and retraining of neural networks, while processing data in real life using the trained and retrained neural networks locally at the surveillance system. The surveillance system can determine a change of location and can retrain the neural network and/or initiate the retraining of the neural network remotely, such as on a cloud server, to retrain the neural network based on new image and/or video data taken from the new location. The remote server can transmit the retrained neural network and/or weights for nodes of the retrained neural network back to the surveillance system. The surveillance system can then update its neural network and process future image and/or video data based on the retrained neural network and/or weights.

Growing community of inventors

Sunnyvale, CA, United States of America

Haoyu Wu