OmniDet: Omnidirectional Object Detection via Fisheye Camera Adaptation
Chih–Chung Hsu, Wei-Hao Huang, Wen-Hai Tseng, Ming-Hsuan Wu, Ren-Jung Xu, Chia-Ming Lee · 2024
Detecting objects in stationary scenes using fisheye cameras poses challenges due to fluctuations in object sizes and distortions at different image locations, which can degrade the accuracy of existing classifiers. To address the unique challenges posed by fisheye cameras in stationary object detection scenarios, we introduce FisheyeAdapt, a novel framework that seamlessly integrates tailored post-processing techniques with distortionaware training strategies, enabling robust and precise object recognition in highly distorted fisheye imagery. We present OmniDet (Scene Context-Aware LEarning for Fisheye), a novel approach that dynamically adjusts confidence thresholds based on object categories and sizes, while leveraging scene context-aware model training. Through extensive experiments, we demonstrate that OmniDet consistently improves the performance of object detection across various fisheye camera-based models, showcasing its wide applicability and effectiveness. Extensive experiments demonstrate the effectiveness of our method.