A compressed domain video saliency detection algorithm, which employs global and local spatiotemporal (GLST) features, is proposed in this work. We first conduct partial decoding of a compressed video bitstream to obtain motion vectors and DCT coefficients, from which GLST features are extracted. More specifically, we extract the spatial features of rarity, compactness, and center prior from DC coefficients by investigating the global color distribution in a frame. We also extract the spatial feature of texture contrast from AC coefficients to identify regions, whose local textures are distinct from those of neighboring regions. Moreover, we use the temporal features of motion intensity and motion contrast to detect visually important motions. Then, we generate spatial and temporal saliency maps, respectively, by linearly combining the spatial features and the temporal features. Finally, we fuse the two saliency maps into a spatiotemporal saliency map adaptively by comparing the robustness of the spatial features with that of the temporal features. Experimental results demonstrate that the proposed algorithm provides excellent saliency detection performance, while requiring low complexity and thus performing the detection in real-time.
|Number of pages||15|
|Journal||Journal of Visual Communication and Image Representation|
|State||Published - Feb 2016|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No. NRF-2015R1A2A1A10055037 ).
© 2015 Elsevier Inc. All rights reserved.
- Compressed domain
- Image analysis
- Image understanding
- Motion analysis
- Partial decoding
- Spatiotemporal feature
- Video saliency detection
- Visual attention