Abstract: Described herein are systems and methods for object detection to achieve hard real-time performance with low latency. Real-time object detection frameworks are disclosed. In one or more embodiments, a framework comprises a first CPU core, a second CPU core, and a plurality of shaves. In one or more embodiments, the first CPU core handles general CPU tasks, while the second CPU core handles the image frames from a camera sensor and computation task scheduling. In one or more embodiments, the scheduled computation tasks are implemented by the plurality of shaves using at least one object-detection model to detect an object in an image frame. In one or more embodiments, computation results from the object-detection model with a higher detection probability is used to form an output for object detection. In one or more embodiments, the object-detection models share some parameters for smaller size and higher implementing speed.

Abstract: An information processing method, a device, a system, and a storage medium. The information processing method includes: an AI camera first obtains real-time data in a unmanned retail scenario and performs a front-end processing on the real-time data based on a neural network model, where the front-end processing includes any one or more of commodity identifying and human body monitoring, and then transmits a result of the front-end processing to a server, where the result of the front-end processing is used to trigger the server to perform face recognition and/or determine a flow direction of a commodity according to the result of the front-end processing. The cost of the entire unmanned retail distributed system and the pressure on data transmission bandwidth can be reduced, and system scalability as well as the performance of the solution to the unmanned retail can be improved effectively.

Abstract: Described herein are systems and methods for object detection to achieve hard real-time performance with low latency. Real-time object detection frameworks are disclosed. In one or more embodiments, a framework comprises a first CPU core, a second CPU core, and a plurality of shaves. In one or more embodiments, the first CPU core handles general CPU tasks, while the second CPU core handles the image frames from a camera sensor and computation task scheduling. In one or more embodiments, the scheduled computation tasks are implemented by the plurality of shaves using at least one object-detection model to detect an object in an image frame. In one or more embodiments, computation results from the object-detection model with a higher detection probability is used to form an output for object detection. In one or more embodiments, the object-detection models share some parameters for smaller size and higher implementing speed.

Abstract: The present application provides a method, an apparatus, a device and a system for processing commodity identification and a storage medium, where the method includes: receiving image information transmitted by a camera apparatus and a distance signal transmitted by a distance sensor corresponding to the camera apparatus; determining a start frame and an end frame for a pickup behavior of a user according to the image information and the distance signal; and determining, according to the start frame and the end frame for the pickup behavior of the user, information of a commodity taken by the user. By performing a commodity identification on the start frame and the end frame for the pickup behavior of the user, and determining the information of the commodity taken by the user, commodity identification efficiency is effectively improved.

Abstract: The present application provides a method, an apparatus, a device and a system for processing commodity identification and a storage medium, where the method includes: receiving image information transmitted by a camera apparatus and a distance signal transmitted by a distance sensor corresponding to the camera apparatus; determining a start frame and an end frame for a pickup behavior of a user according to the image information and the distance signal; and determining, according to the start frame and the end frame for the pickup behavior of the user, information of a commodity taken by the user. By performing a commodity identification on the start frame and the end frame for the pickup behavior of the user, and determining the information of the commodity taken by the user, commodity identification efficiency is effectively improved.

Abstract: An information processing method, a device, a system, and a storage medium. The information processing method includes: an AI camera first obtains real-time data in a unmanned retail scenario and performs a front-end processing on the real-time data based on a neural network model, where the front-end processing includes any one or more of commodity identifying and human body monitoring, and then transmits a result of the front-end processing to a server, where the result of the front-end processing is used to trigger the server to perform face recognition and/or determine a flow direction of a commodity according to the result of the front-end processing. The cost of the entire unmanned retail distributed system and the pressure on data transmission bandwidth can be reduced, and system scalability as well as the performance of the solution to the unmanned retail can be improved effectively.

Abstract: The present disclosure provides a method and apparatus for sorting related searches RS, wherein the method comprises: obtaining a query and obtaining RSes corresponding to the query; determining estimated click rates of the RSes respectively according to a click estimating model obtained from pre-training; sorting the RSes in a descending order according to the estimated click rates. The solutions of the present disclosure can improve the accuracy of the sorting results.

Abstract: The present disclosure provides a method and apparatus for sorting related searches RS, wherein the method comprises: obtaining a query and obtaining RSes corresponding to the query; determining estimated click rates of the RSes respectively according to a click estimating model obtained from pre-training; sorting the RSes in a descending order according to the estimated click rates. The solutions of the present disclosure can improve the accuracy of the sorting results.