AI-Driven Relocation Tracking in Dynamic Kitchen Environments

2024-11-19

International Conference on Computer and Knowledge Engineering

AI Summary

To address insufficient relocalization robustness in dynamic kitchen environments—caused by frequent object motion, partial occlusions, and illumination variations—this paper proposes a lightweight frame-to-frame association tracking algorithm. Methodologically, it integrates YOLOv5 fine-tuned on a custom kitchen dataset for object detection and introduces a frame-scoring mechanism that evaluates candidate matching frames based on inter-frame positional consistency and visual feature similarity, enabling optimal frame association without complex temporal modeling. This design balances accuracy and computational efficiency. Evaluated on a real-world kitchen video dataset, the system achieves 97.72% accuracy, 95.83% precision, and 96.84% recall, significantly improving stability and adaptability in tracking object spatial displacements. The approach provides a deployable visual perception solution for service robots operating in complex, dynamic indoor settings.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 which 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, introducing a frame-scoring algorithm which 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 of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes.

Problem

• Develops AI algorithm for robot navigation in dynamic kitchens.
• Tracks object relocation using YOLOv5 and frame-scoring method.
• Overcomes challenges like lighting variations and partial occlusions.

Innovation

• YOLOv5 architecture for object detection
• Frame-scoring algorithm for relocation tracking
• High accuracy in dynamic kitchen environments