GWO-ATDL-SBD: A GREY WOLF OPTIMIZED ADAPTIVE TEMPORAL DEEP LEARNING FRAMEWORK FOR ROBUST SHOT BOUNDARY DETECTION - Banik, Tushar; Chakraborty, Saptarshi; Thounaojam, Dalton Meitei; Singh, Sapam Jitu; Rajkumar, Raju (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Shot Boundary Detection (SBD) is an essential task in video content analysis but accurately detecting both abrupt and gradual scene changes is still a problem due to different lightings, camera motions, and varied scene contents. This paper introduces a hybrid framework named GWO-ATDL-SBD that combines Grey Wolf Optimizer (GWO) with adaptive temporal deep learning for performing robust shot boundary detection. At first, multi-cue features such as edge energy difference, motion vector entropy, and color histogram distance are extracted to represent different visual characteristics in combination. These features are combined dynamically using weights obtained by GWO and passed to a Bi-directional Long Short-Term Memory (Bi-LSTM) network to capture the frame-wise temporal dependencies. In contrast to the traditional methods having fixed thresholds, our method uses GWO not only to find the optimal feature fusion weights and classifier parameters but also decision thresholds for adaptive and accurate recognition of both cuts and transitions. Extensive experiments on standard video datasets show that the proposed method substantially surpasses the existing SBD methods in terms of precision, recall, and F1-score. The findings reveal the potential of marrying evolutionary optimization with temporal deep learning for tackling complex video transition detection problems.