  Generic Event Boundary Detection (GEBD) task aims to recognize generic,
taxonomy-free boundaries that segment a video into meaningful events. Current
methods typically involve a neural model trained on a large volume of data,
demanding substantial computational power and storage space. We explore two
pivotal questions pertaining to GEBD: Can non-parametric algorithms outperform
unsupervised neural methods? Does motion information alone suffice for high
performance? This inquiry drives us to algorithmically harness motion cues for
identifying generic event boundaries in videos. In this work, we propose
FlowGEBD, a non-parametric, unsupervised technique for GEBD. Our approach
entails two algorithms utilizing optical flow: (i) Pixel Tracking and (ii) Flow
Normalization. By conducting thorough experimentation on the challenging
Kinetics-GEBD and TAPOS datasets, our results establish FlowGEBD as the new
state-of-the-art (SOTA) among unsupervised methods. FlowGEBD exceeds the neural
models on the Kinetics-GEBD dataset by obtaining an<EMAIL_ADDRESS>score of 0.713 with
an absolute gain of 31.7% compared to the unsupervised baseline and achieves an
average F1 score of 0.623 on the TAPOS validation dataset.
