Computer vision and machine learning techniques for item tracking

Techniques are described for processing digital video data using one or more machine learning models to determine an outcome of an item placement operation within a fulfillment center environment. Video data is processed using one or more machine learning models to determine an estimated likelihood...

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Bibliographische Detailangaben
Hauptverfasser:	Stallman, Timothy, Gallaudet, Elisha
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING CONVEYING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY FUNCTIONAL ELEMENTS OF SUCH SYSTEMS HANDLING RECORD CARRIERS HANDLING THIN OR FILAMENTARY MATERIAL IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PACKING PERFORMING OPERATIONS PHYSICS PICTORIAL COMMUNICATION, e.g. TELEVISION PNEUMATIC TUBE CONVEYORS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS REGULATING SHOP CONVEYOR SYSTEMS STORING TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING ORTIPPING TRANSPORTING
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Beschreibung
Zusammenfassung:	Techniques are described for processing digital video data using one or more machine learning models to determine an outcome of an item placement operation within a fulfillment center environment. Video data is processed using one or more machine learning models to determine an estimated likelihood that an occurrence of a particular fulfillment center operation is depicted within the two or more instances of digital video data. Upon determining that the estimated likelihood exceeds a predefined threshold confidence level, the video data is processed using second one or more machine learning models to determine a bin placement prediction and a confidence value. A data repository for a control system for the fulfillment center environment is updated, based on the bin placement prediction and the confidence value.