International Conference on Computer and Knowledge Engineering

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments

Authors :

Arash Nasr Esfahani¹ Hamed Hosseini² Mehdi Tale Masouleh³ Ahmad Kalhor⁴ Hedieh Sajedi⁵

1- University of Tehran 2- University of Tehran 3- University of Tehran 4- University of Tehran 5- University of Tehran

Keywords :

Object Detection،Relocation Tracking،AI2-THOR،YOLOv5،Computer Vision

Abstract :

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm that can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged, and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, which introduces a frame-scoring algorithm that calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy rate of 97.72% precision of 95.83% and recall of 96.84% for this algorithm which highlight the efficacy of the model in detecting spatial changes.