Edge detection in dynamic video environments remains challenging due to noise, texture clutter, and real-time processing constraints. To address this, we propose a novel fusion framework that synergistically combines multi-scale Gabor filtering with a directionally enhanced Sobel operator, followed by adaptive weighting based on local consistency. Unlike deep learning approaches, our method requires no training data and operates efficiently on standard hardware. Evaluated on the BSDS500 and DAVIS 2017 datasets, our approach achieves an F1-score of 88.8%, outperforming classical baselines such as Canny (81.2%) and Sobel (75.8%) by 7.6 and 13.0 percentage points, respectively. Moreover, it attains an edge localization error of only 1.42 pixels, demonstrating superior spatial precision. The integration of FFT-based and separable convolutions enables real-time performance at over 45 FPS on 480p video streams, making it suitable for resource-constrained applications like autonomous drones or embedded surveillance systems. A sensitivity analysis confirms robustness, with F1-score variations under ±1.5% across parameter perturbations. By bridging frequency-domain selectivity with gradient-based sharpness, our method delivers both high accuracy and computational efficiency—offering a practical solution for real-world edge-aware video analytics where reliability, speed, and interpretability are critical.
Gao et al. (Sat,) studied this question.