Object Counting using KAZE Features Under Different Lighting Conditions for Inventory Management

Inventory management in an automated industrial environment is the foremost requirement in order to shorten the gap between demand and supply. It comprises of object identification, localization, and counting. This paper introduces an approach for object counting in automated inventory management using KAZE features under different lighting conditions. Firstly, the prototype image and real-time inventory feed as the scene image are captured for detection of KAZE features. The detected features in the prototype image are subjected to density based scanning clustering algorithm. The KAZE features of each cluster obtained in the prototype image are mapped with the KAZE features of inventory feed. The mapped features in inventory feed are again subjected to density based scanning clustering algorithm. The clusters obtained in the inventory feed are then processed by Homography transform. Homography transform generates the predictions for object locations by projecting prototype corners in the inventory feed. The Homography transform projection results in rectangular box polygons in the inventory feed for the tentative location of prototype instances. Since there may be multiple predictions for a single object instance, the predicted object locations are integrated by density based scanning clustering algorithm to the centroids of these rectangular box polygons. It provides the exact location of prototype instances. Finally, the count is obtained. The graphical user interface for inventory management is also designed which exhibits user-friendly attributes. The proposed approach has also been compared with the previously developed approaches of object counting in automated inventory management. The experimental results state that the proposed approach outperforms the existing ones in the presence of different lighting conditions such as low-light or dim-light and bright light.