  We propose a novel approach for automatic chaptering of TV newscast videos,
addressing the challenge of structuring and organizing large collections of
unsegmented broadcast content. Our method integrates both audio and visual cues
through a two-stage process involving frozen neural networks and a trained LSTM
network. The first stage extracts essential features from separate modalities,
while the LSTM effectively fuses these features to generate accurate segment
boundaries. Our proposed model has been evaluated on a diverse dataset
comprising over 500 TV newscast videos of an average of 41 minutes gathered
from TF1, a French TV channel, with varying lengths and topics. Experimental
results demonstrate that this innovative fusion strategy achieves state of the
art performance, yielding a high precision rate of 82% at IoU of 90%.
Consequently, this approach significantly enhances analysis, indexing and
storage capabilities for TV newscast archives, paving the way towards efficient
management and utilization of vast audiovisual resources.
