This paper proposes a spatio-temporal attentive mechanism to detect events from video sequences of natural scenes of dynamic en- vironments. More specifically, we wish to detect a visual event within a cluttered scene, without intensive training of the algorithm. In contrast to the event detection methods used in the literature, which drive atten- tion based on motion and spatial location hypothesis, in our approach the visual attention is region-driven as well as feature-driven. For this purpose a two stages attention mechanism is proposed. In a first phase spatio-temporal activity analysis extracts key frames from the image se- quence and selects salient areas within these frames. For this purpose, next to a peak detection method, we employed a change-point detection method, which exists both in a batch as well as a incremental version. Consequently, these areas are further processed to determine the most interesting active region, based on a newly defined region saliency mea- sure. The results of the proposed approach are reported using natural image sequence of a crowded train station.
Geerinck, T, Sahli, H, Paletta, L (ed.) & Rome, E (ed.) 2007, 'Region-oriented Visual Attention-based Activity Detection', Attention in Cognitive Systems, pp. 481-496.
Geerinck, T., Sahli, H., Paletta, L. (Ed.), & Rome, E. (Ed.) (2007). Region-oriented Visual Attention-based Activity Detection. Attention in Cognitive Systems, 481-496.
@article{e3e0da1217944b93a059168b8d5b8033,
title = "Region-oriented Visual Attention-based Activity Detection",
abstract = "This paper proposes a spatio-temporal attentive mechanism to detect events from video sequences of natural scenes of dynamic en- vironments. More specifically, we wish to detect a visual event within a cluttered scene, without intensive training of the algorithm. In contrast to the event detection methods used in the literature, which drive atten- tion based on motion and spatial location hypothesis, in our approach the visual attention is region-driven as well as feature-driven. For this purpose a two stages attention mechanism is proposed. In a first phase spatio-temporal activity analysis extracts key frames from the image se- quence and selects salient areas within these frames. For this purpose, next to a peak detection method, we employed a change-point detection method, which exists both in a batch as well as a incremental version. Consequently, these areas are further processed to determine the most interesting active region, based on a newly defined region saliency mea- sure. The results of the proposed approach are reported using natural image sequence of a crowded train station.",
keywords = "Event detection, Activity measure, Visual attention, Region-oriented",
author = "Thomas Geerinck and Hichem Sahli and L. Paletta and E. Rome",
note = "L. Paletta and E. Rome",
year = "2007",
language = "English",
pages = "481--496",
journal = "Attention in Cognitive Systems",
}