Aiming at the problems of non-uniform motion blur， illumination changes， and background interference in the detection for load swing angle of bridge cranes， a real-time monocular vision-based method for load swing angle detection was proposed. In this method， spherical markers were utilized as feature targets and a collaborative framework integrating Channel and Spatial Reliability Tracker with Adaptive Feature Tracking （CSRT-AFT） and Gradient Hierarchical Adaptive Circle Detection （GHACD） algorithm was constructed. Firstly， robust tracking was achieved by CSRT-AFT through a dynamic trajectory filtering and feature adaptation mechanism； an adaptive multi-modal trajectory filtering was designed， and the filtering strategy was switched intelligently based on curvature change rate and acceleration mutation index， so as to suppress intense motion-induced trajectory jitter； dynamic Oriented FAST and Rotated BRIEF （ORB） feature extraction， weighted K-Dimensional （K-D） feature screening， and Least Median of Squares （LMedS） were combined for feature matching and elastic template updating， thereby enhancing feature matching stability under motion blur and complex illumination. Secondly， for rapid and precise target localization， based on using image preprocessing for robustness enhancement， the GHACD algorithm achieves sub-pixel level fast circle detection through gradient field-guided circle center candidate generation， multi-stage probability sampling， and geometric constraint verification. Finally， a load swing angle measurement model was established on the basis of the bridge crane’s workspace. Experimental results demonstrate that this method can detect the load target’s swing angle stably under various trolley speeds and complex conditions including illumination changes and occlusions， and improve detection accuracy and real-time performance significantly.