Abstract: Embodiments herein provide a method for detecting and tracking objects using a Computer Vision (CV) model. The method includes (i) receiving media content from a user device, (ii) rendering the media content into one or more frames with pre-defined dimensions, (iii) enabling a user to select one or more mouse points on at least one of the one or more frames to define a Region of Interest (ROI) and line crossings along with directions, (iv) converting the one or more mouse points into coordinates, (v) identifying coordinate details of the region of interest and the line crossings along with the directions, and (vi) tracking each object in the region of interest and the line crossings along with the directions based on the coordinate details and by assigning identification numbers to each object in the at least one of the one or more frames.

Abstract: A system for personalizing a computer vision (CV) model based on use cases is provided. The method includes (i) segmenting media content received from a user device into a sequence of individual frames, (ii) extracting a first frame, (iii) providing a plurality of preference segments to the user device to enable the user to select a preference, (iv) generating an event by enabling the user to interact with the first frame using a mouse, (v) configuring the CV model automatically, (vi) converting the one or more mouse clicks performed on the first frame into coordinates, (vi) converting the input into a compatible format by processing the input of the user with every click of ‘y’ or ‘q’ by the user on the keyboard, (vii) personalizing the CV model based on use cases by crystalizing the input of the CV model to perform interpretation/prediction, upon receiving confirmation from the user.

Abstract: A system for tracking inventory inside a warehouse with put-away accuracy is provided. The system 100 includes unmanned aerial vehicle (UAV) 102 including image capturing device 102A, warehouse 104, inventory tracking unit 106, user device 108, cloud server 110, and network 112. The UAV 102 is configured to capture media contents of pre-defined space within the warehouse 104 using image capturing device 102A. The pre-defined space includes rack bays with unique rack bay identifier and inventory items stocked on rack bays with pallet identifier that is similar to corresponding unique rack bay identifier. The inventory tracking unit 106 determines inventory data including empty space, inventory mismatch, and inventory record by processing the media contents using machine learning models 106A-B. The inventory tracking unit 106 sends empty space alert and mismatch alert to user and transmits inventory data to cloud server 110 and user device 108 through network 112.