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Performance evaluation and integration of distortion mitigation methods for fisheye video object detection Du, John Benedict; Mayuga, Gian Paolo; Guico, Maria Leonora
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp743-758

Abstract

The distortion observed in fisheye cameras has proven to be a persistent challenge for numerous state-of-the-art object detection algorithms, instigating the development of various techniques aimed at mitigating this issue. This study aims to evaluate various methods for mitigating distortion in fisheye camera footage and their impact on video object detection accuracy and speed. Using Python, OpenCV, and third-party libraries, the researchers modified and optimized said methods for video input and created a framework for running and testing different distortion correction methods and object detection algorithm configurations. Through experimentation with different datasets, the study found that undistorting the image using the longitude-latitude correction with the YOLOv3 object detector provided the best results in terms of accuracy (PASCAL: 68.9%, VOC-360: 75.1%, WEPDTOF: 15.9%) and speed (38 FPS across all test sets) for fisheye footage. After measuring the results to determine the best configuration for video object detection, the researchers also developed a desktop application that incorporates these methods and provides real-time object detection and tracking functions. The study provides a foundation for improving the accuracy and speed of fisheye camera setups, and its findings can be valuable for researchers and practitioners working in this field.