Improvement of object classification in image sequences

1997 ◽  
Author(s):  
D. Ernst ◽  
H. Gross ◽  
D. Stricker ◽  
Ulrich Thoennessen
Keyword(s):  
Object Classification ◽  
Image Sequences

Using Dempster-Shafer Methods for Object Classification in the Theater Ballistic Missile Environment

1999 ◽  
Author(s):  
Kimberly Coombs ◽  
Debra Freel ◽  
Douglas Lampert ◽  
Steven Brahm
Keyword(s):  
Object Classification ◽  
Ballistic Missile

scholarly journals A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Visual Attention ◽  
Optical Flow ◽  
Prediction Models ◽  
Apparent Movement ◽  
Image Sequences ◽  
Motion Information ◽  
Accuracy Improvement ◽  
Flow Maps ◽  
Current State ◽  
Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

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