Abstract: The technology disclosed relates to detecting events and identifying items in detected events in an area of real space in a shopping store including a cashier-less checkout system. The system comprises an image sensor assembly comprising at least one narrow field of view (NFOV) image sensor and at least one wide field of view (WFOV) image sensor. The at least one NFOV image sensor produces raw image data of first-resolution frames of a corresponding field of view and the at least one WFOV image sensor produces raw image data of second-resolution frames of a corresponding field of view. The system comprises logic to provide at least a portion of a sequence of second-resolution frames produced by the WFOV image sensor to an event detection device configured to detect (i) a particular event and (ii) a location of the particular event in the area of real space.

Abstract: The technology disclosed relates to systems and methods for predicting a path of a subject in an area of real space in a shopping store including a cashier-less checkout system. The system comprises a plurality of sensors producing respective sequences of frames of corresponding fields of view in the real space. The system comprises an identification device comprising logic to identify, for a particular subject, a determined path in the area of real space over a period of time using the sequences of frames produced by sensors in the plurality of sensors. The system comprises a path prediction device to predict path of new subjects in the area of real space. The system comprises a layout generation device comprising logic to change a preferred placement of a particular item, in dependence on the predicted path.

Abstract: Systems and methods for tracking and re-identifying tracked subjects in an area of real space are disclosed. The method includes generating first and second reidentification feature vectors of a first subject identified from a first time interval from the respective first and second sequences of images as obtained from the first time interval. The method includes generating third and fourth reidentification feature vectors of a second subject identified from a second time interval from the respective first and second sequences of images. The method includes matching a second subject identified from a second time interval with the first subject identified from the first time interval when at least one of a first similarity score between the first and the third reidentification feature vectors and a second similarity score between the second and the fourth reidentification feature vectors is above a pre-defined threshold.