Drishti Technologies, Inc.

Apple Inc.

R4n63r Capital LLC

Devashish Shankar

Krishnendu Chaudhury

Ananya Honnedevasthana Ashok

Prasad Narasimha Akella

Ananth Uggirala

Sujay Venkata Krishna Narumanchi

Sujay Narumanchi

Ritesh Jain

David Scott Prager

Sameer Gupta

Zakaria Ibrahim Assoul

Yash Raj Chhabra

Ashish Mehra

Ashish Gupta

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of certificates for each instance of a subject product or service. The certificates can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.

Automated certificate systems and methods

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for process validation, anomaly detection and in-process quality assurance.

Real time anomaly detection systems and methods

An event detection method can include encoding a plurality of training video snippets into low dimensional descriptors of the training video snippets in a code space. The low dimensional descriptors of the training video snippets can be decoded into corresponding reconstructed video snippets. One or more parameters of the encoding and decoding can be adjusted based on one or more a loss functions to reduce a reconstruction error between the one or more training video snippets and the corresponding one or more reconstructed video snippets, to reduce a class entropy of the plurality of event classes of the code space, to increase fit of the training video snippet, and/or to increase compactness of the code space. The method can further include encoding one or more labeled video snippets of a plurality of event classes into low dimensional descriptors of the labeled video snippets in the code space. The plurality of event classes can be mapped to class clusters corresponding to the low dimensional descriptors of the labeled video snippets. After training, query video snippets can be encoded into corresponding low dimensional descriptors in the code space. The low dimensional descriptors of the query video snippets can be classified based on their respective proximity to a nearest one of a plurality of class cluster of the code space. An event class of the query video snippet can be determined based on the class cluster classification.

Almost unsupervised cycle and action detection

Techniques for detecting cycle data can include determining object properties and motion properties in a set of consecutive frames of a sensor stream. The cycle data can be determined from the object properties and motion properties without detecting constituent objects. The object properties and motion properties enable improved detection of cycle data in the presence of different object poses, different positions of the object, partial occlusion of the object, varying illumination, variation in the background, and or the like.

Cycle detection techniques

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing, restaurants and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for establishing traceability.

Traceability systems and methods

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Gwalior, India

Devashish Shankar