Real-Time Panoramic Image Stitching Framework based on IoT for Intelligent Surveillance Systems
DOI:
https://doi.org/10.4108/eetiot.13826Keywords:
ORB, Edge Computing, Image Processing, Panorama Stitching, IoT, ESP32-CAM, Feature Extraction, SIFT, BRISK, Smart SurveillanceAbstract
INTRODUCTION: Panoramic image stitching is a fundamental technique in intelligent vision systems for generating wide field-of-view images from multiple overlapping images. The integration of panoramic imaging with Internet of Things (IoT) technology has enabled advanced applications such as intelligent surveillance, robotics, autonomous monitoring, and smart city systems; however, real-time panoramic image generation on resource-constrained IoT devices remains challenging due to image noise, motion blur, illumination variations, and limited computational resources.
OBJECTIVES: This study aims to develop a lightweight and efficient IoT-based panoramic image stitching framework that achieves robust stitching accuracy while maintaining low computational complexity for real-time edge-assisted vision applications.
METHODS: The proposed framework integrates image acquisition using an ESP32-CAM module with cloud-based image processing and employs ORB, SIFT, BRISK, and a Hybrid ORB–SIFT feature extraction approach for keypoint detection, descriptor generation, feature matching, homography estimation, image warping, and panoramic image reconstruction. Performance was evaluated on overlapping image datasets corrupted with Gaussian noise, Salt-and-Pepper noise, and motion blur using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), execution time, feature matching statistics, and keypoint analysis.
RESULTS: Experimental results demonstrate that SIFT provides superior stitching robustness and panorama quality under noisy conditions, whereas ORB offers significantly lower computational complexity and faster execution. The proposed Hybrid ORB–SIFT framework effectively combines the strengths of both methods, achieving 1335 valid feature matches with an execution time of 1.596 s, while maintaining high stitching accuracy and robustness under challenging imaging conditions.
CONCLUSION: The proposed IoT-enabled Hybrid ORB–SIFT panoramic image stitching framework provides an effective balance between computational efficiency and stitching accuracy, making it a reliable and lightweight solution for real-time panoramic surveillance, autonomous monitoring, and next-generation IoT-based intelligent vision applications.
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