Optimization for Camera Self-calibration Based on Horizon Detection in Road Scenes

doi:10.3969/j.issn.1006-2475.2025.02.011

Abstract

Abstract: The current camera calibration in traffic scenes relies mainly on the key information of the road scene and relies on redundant information such as road dashed lines， parallel lines， etc. to optimize the camera calibration parameters. However， due to the limited information of the scene， the range of vanishing points cannot be fixed， and at the same time， due to the existence of the camera spin angle， the results of the camera calibration have a certain degree of error. Starting from the horizon detection， a deep learning key point detection-based horizon detection algorithm improves the accuracy to 82.46%. Subsequently， the camera self-calibration parameters are optimized by correcting the camera spin angle based on horizon detection and providing stricter constraints by using the horizon. The experimental results show that after correcting the camera spin angle and providing stronger constraints by using the horizon， the camera self-calibration parameters obtain faster convergence and a minimum of 1.79% error.

Key words: , monocular camera； camera calibration； deep learning； horizon line detection； camera spin angle

CLC Number:

TP399

HE Guotao1, ZHAO Chunhui2, LIU Zhenyu1, WANG Long1. Optimization for Camera Self-calibration Based on Horizon Detection in Road Scenes[J]. Computer and Modernization, 2025, 0(02): 77-85.

References

［1］ GANBOLD U， SATO J， AKASHI T. Coarse-to-fine evolutionary method for fast horizon detection in maritime images［J］. IEICE Transactions on Information and Systems， 2021，E104.D（12）:2226-2236.
［2］ ZARDOUA Y， ASTITO A， BOULAALA M. A survey on horizon detection algorithms for maritime video surveillance: Advances and future techniques［J］. The Visual Computer， 2023，39（1）:197-217.
［3］ JEONG C， YANG H S， MOON K. A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection［J］. Multidimensional Systems and Signal Processing， 2019，30（3）:1187-1204.
［4］ JEONG C Y， YANG H S， MOON K D. Horizon detection in maritime images using scene parsing network［J］. Electronics Letters， 2018，54（12）:760-762.
［5］ FU J， LI F， ZHAO J H. Real-time infrared horizon detection in maritime and land environments based on hyper-Laplace filter and convolutional neural network［J］. IEEE Transactions on Instrumentation and Measurement， 2023，72. DOI: 10.1109/TIM.2023.3282656.
［6］ SANDRU A， KUJALA P， VISALA A. Horizon detection and tracking in sea-ice conditions using machine vision［J］. IFAC-PapersOnLine， 2023，56（2）:6724-6730.
［7］ HOU J L， LI B K. An improved algorithm for horizon detection based on OSTU［C］// 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE， 2015:414-417.
［8］ AHMAD T， BEBIS G， NICOLESCU M， et al. Horizon line detection using supervised learning and edge cues［J］. Computer Vision and Image Understanding， 2020，191. DOI: 10.1016/j.cviu.2019.102879.
［9］ LUO Y L， ZHANG G L， LI L， et al. Attention-based two-stage U-Net horizon tracking［J］. IEEE Geoscience and Remote Sensing Letters， 2022，19. DOI: 10.1109/LGRS.2022.3217376.
［10］ XU Y， YAN H T， MA Y， et al. Graph-based horizon line detection for UAV navigation［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2021，14:11683-11698.
［11］叶继华，时淑霞，李汉曦，等. 一种结合深度学习和随机森林的地平线检测方法［J］. 系统仿真学报， 2018，30（7）:2507-2514.
［12］ ZHANG Y G， SU Y N， YANG J， et al. When Dijkstra meets vanishing point: A stereo vision approach for road detection［J］. IEEE Transactions on Image Processing， 2018，27（5）:2176-2188.
［13］ KLUGER F， ACKERMANN H， YANG M Y， et al. Temporally consistent horizon lines［C］//2020 IEEE International Conference on Robotics and Automation （ICRA）. IEEE， 2020:3161-3167.
［14］吴建平，陈珂，刘业. 融合随机抽样一致性和Hough变换的实时消失点检测［J］. 计算机辅助设计与图形学学报， 2022，34（8）:1238-1251.
［15］ KANHERE N K， BIRCHFIELD S T. A taxonomy and analysis of camera calibration methods for traffic monitoring applications［J］. IEEE Transactions on Intelligent Transportation Systems， 2010，11（2）:441-452.
［16］宋洪军，陈阳舟，郜园园. 非结构化道路中基于均质雾天的摄像机动态标定算法［J］. 计算机辅助设计与图形学学报， 2013，25（7）:1060-1073.
［17］ BHARDWAJ R， TUMMALA G K， RAMALINGAM G， et al. Autocalib: Automatic traffic camera calibration at scale［J］. ACM Transactions on Sensor Networks （TOSN）， 2018，14（3-4）. DOI: 10.1145/3199667.
［18］ LEPETIT V， MORENO-NOGUER F， FUA P. EPnP: An accurate O（n） solution to the PnP problem［J］. International Journal of Computer Vision， 2009，81（2）:155–166.
［19］ LIU D F， CHEN L F. SECPNet—Secondary encoding network for estimating camera parameters［J］. The Visual Computer， 2022，38（5）:1689–1702.
［20］ ZHENG Y， PENG S L. A practical roadside camera calibration method based on least squares optimization［J］. IEEE Transactions on Intelligent Transportation Systems， 2014，15（2）:831-843.
［21］ SOCHOR J， JURÁNEK R， HEROUT A. Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement［J］. Computer Vision and Image Understanding， 2017，161:87-98.
［22］ ZUNAIR H， HAMZA A B. Sharp U-Net: Depthwise convolutional network for biomedical image segmentation［J］. Computers in Biology and Medicine， 2021，136. DOI:10.1016/j.compbiomed.2021.104699.
［23］ DUBSKÁ M， HEROUT A. Real projective plane mapping for detection of orthogonal vanishing points［C］// Proceedings of the 24th British Machine Vision Conference. BMVA， 2013. DOI:10.5244/C.27.90.
［24］ DUBSKÁ M， HEROUT A， HAVEL J. PClines—line detection using parallel coordinates［C］// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2011:1489-1494.
［25］ LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. IEEE， 2017:2999-3007.
［26］王伟，张朝阳，唐心瑶，等. 道路场景下相机自动标定及优化算法［J］. 计算机辅助设计与图形学学报， 2019，31（11）:1955-1962.
［27］ SOCHOR J， JURÁNEK R， ŠPAŇHEL J， et al. Comprehensive data set for automatic single camera visual speed measurement［J］. IEEE Transactions on Intelligent Transportation Systems， 2019，20（5）:1633-1643.
［28］ TANG X Y， WANG W， SONG H S， et al. Novel optimization approach for camera calibration in traffic scenes［J］. Transportation Research Record， 2023，2677（3）:1048-1066.

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