Visual Relationship Detection with AI

Abstract

Visual Relationship Detection (VRD) enables machines to go beyond object detection and recognize how objects interact within an image. This project combines a pre-trained YOLOv2 deep learning model in MATLAB with heuristic rules to detect object-object relationships, such as “person riding a bicycle” or “dog under table.” Additionally, we evaluate multiple VRD 2 approaches using a Zwicky Box based on technical criteria like accuracy, interpretability, and computational cost. The project is fully documented in a wiki, supported by visualizations.

Introduction

Recognizing object relationships is crucial for scene understanding in autonomous systems, robotics, and surveillance. object detection algorithms such as YOLO only locate objects, but not how they interact. VRD adds this missing layer of contextual understanding.

This project:

Implements a rule-based VRD system in MATLAB using YOLOv2,

Explains each code segment clearly for reproducibility,

Compares different VRD methods using a morphological matrix,

Documents all results in a structured scientific format.

Task

Visual Relationship Detection: This involves identifying the relationships between objects within an image, such as “person riding a bicycle” or “dog under a table”. This task requires not just recognizing the objects but understanding their interactions.

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