  The huge amount of video data produced daily by camera-based systems, such as
surveilance, medical and telecommunication systems, emerges the need for
effective video summarization (VS) methods. These methods should be capable of
creating an overview of the video content. In this paper, we propose a novel VS
method based on a Generative Adversarial Network (GAN) model pre-trained with
human eye fixations. The main contribution of the proposed method is that it
can provide perceptually compatible video summaries by combining both perceived
color and spatiotemporal visual attention cues in a unsupervised scheme.
Several fusion approaches are considered for robustness under uncertainty, and
personalization. The proposed method is evaluated in comparison to
state-of-the-art VS approaches on the benchmark dataset VSUMM. The experimental
results conclude that SalSum outperforms the state-of-the-art approaches by
providing the highest f-measure score on the VSUMM benchmark.
