Title

A part-based spatial and temporal aggregation method for dynamic scene recognition

RIS ID

146131

Publication Details

X. Peng, A. Bouzerdoum & S. Phung, "A part-based spatial and temporal aggregation method for dynamic scene recognition," Neural Computing and Applications, 2020.

Abstract

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Existing methods for dynamic scene recognition mostly use global features extracted from the entire video frame or a video segment. In this paper, a part-based method is proposed to aggregate local features from video frames.A pre-trained Fast R-CNN model is used to extract local convolutional features from the regions of interest of training images. These features are clustered to locate representative parts. A set cover problem is then formulated to select the discriminative parts, which are further refined by fine-tuning the Fast R-CNN model. Local features from a video segment are extracted at different layers of the fine-tuned Fast R-CNN model and aggregated both spatially and temporally. Extensive experimental results show that the proposed method is very competitive with state-of-the-art approaches.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s00521-020-05415-3