Tcl Research America Inc.

Guanghan Ning

Haohong Wang

Xiaobo Ren

Zhi Zhang

Wenqiang Bo

Lifan Guo

An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Experience-aware anomaly processing system and method

A method for optimized wake-up strategy via sleeping stage prediction with recurrent neural networks is provided. The method comprises receiving a set of raw sensory data of a user's sleeping status; translating the set of raw sensory data into a plurality of sleeping stages; saving the plurality of sleeping stages as a history of sleeping stages; determining a current sleeping stage; predicting a plurality of future sleeping stages of the user in an upcoming interval; calculating a wakeup score of the current sleeping stage and a wakeup score of each future sleeping stage; determining an optimized sleeping stage for wake up based on the wakeup score through comparing the wakeup score of the current sleeping stage and the wakeup scores of the future sleeping stages; and triggering an alarm impulse to wake up the user at the optimized sleeping stage for wake up.

Method and system for optimized wake-up strategy via sleeping stage prediction with recurrent neural networks

A content retrieval method including: extracting a plurality of fingerprints including a plurality of video fingerprints and audio fingerprints from contents stored in a content database; determining representative video fingerprints of the video frames and representative audio fingerprints of the audio sequences; determining a data rate indicating a storage limitation and a coverage indicating a number of searching results to be returned; storing selected representative video fingerprints and representative audio fingerprints based on the storage limitation indicated by the data rate in a fingerprint database; receiving a query containing at least one of video data and audio data submitted by a user; extracting at least one query fingerprint representing the query; determining a number of fingerprints most matching the query fingerprint based on the coverage to generate search results indicating matching contents represented by the number of most matching fingerprints; and returning the search results to the user.

Method and system for content retrieval based on rate-coverage optimization

A method for a mobile search-ready smart display system containing a smart display having a screen and at least one mobile device having a camera including extracting a plurality of video fingerprints from an ongoing content displayed on the screen. The ongoing content has video frames corresponding to the video fingerprints; determining one or more representative video fingerprints of the video frames from the plurality of video fingerprints; generating one or more encoded representative video fingerprints through encoding the one or more representative video fingerprints into a pixel translucency change; and delivering the one or more encoded representative video fingerprints via a screen-camera communication. The method also includes optimizing the encoding of the one or more representative video fingerprints and the delivering of the one or more encoded representative video fingerprints to find a balance between an error resilience and transmission time.

Mobile search-ready smart display technology utilizing optimized content fingerprint coding and delivery

In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.

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San Jose, CA, United States of America

Guanghan Ning