A Video Retrieval Algorithm Based on Spatio-temporal Feature Curves

2008 ◽  
Author(s):  
Xiuxin Chen ◽  
Kebin Jia ◽  
Xinyue Zhuang
Keyword(s):  
Video Retrieval ◽  
Retrieval Algorithm ◽  
Spatio Temporal ◽  
Temporal Feature

INTEGRATION OF COLOR AND MOTION FEATURES FOR VIDEO RETRIEVAL

2009 ◽  
Vol 23 (02) ◽  
pp. 313-329 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
KUO-HAO CHIN ◽  
HONG-YUAN MARK LIAO
Keyword(s):  
Visual Cues ◽  
Video Retrieval ◽  
Retrieval Algorithm ◽  
Compact Representation ◽  
Key Frame ◽  
Video Clips ◽  
Video Shot ◽  
Motion Features ◽  
Spatio Temporal ◽  
Video Matching

The usefulness of a video database depends on whether the video of interest can be easily located. In this paper, we propose a video retrieval algorithm based on the integration of several visual cues. In contrast to key-frame based representation of shot, our approach analyzes all frames within a shot to construct a compact representation of video shot. In the video matching step, by integrating the color and motion features, a similarity measure is defined to locate the occurrence of similar video clips in the database. Therefore, our approach is able to fully exploit the spatio-temporal information contained in video. Experimental results indicate that the proposed approach is effective and outperforms some existing technique.

Extracting Spatio-temporal Feature for Classification of Event-related Potentials*

2011 ◽  
Vol 38 (9) ◽  
pp. 866-871 ◽  
Author(s):  
Zhi-Hua HUANG ◽  
Ming-Hong LI ◽  
Yuan-Ye MA ◽  
Chang-Le ZHOU
Keyword(s):  
Event Related Potentials ◽  
Related Potentials ◽  
Spatio Temporal ◽  
Temporal Feature

Unmanned vehicle dynamic obstacle detection, tracking and recognition method based on laser sensor

2021 ◽  
Vol 14 (2) ◽  
pp. 239-251
Author(s):  
Hualei Zhang ◽  
Mohammad Asif Ikbal
Keyword(s):  
Feature Fusion ◽  
Obstacle Detection ◽  
Geometric Features ◽  
Recognition Method ◽  
Content Type ◽  
Detection And Tracking ◽  
Spatio Temporal ◽  
Dynamic Obstacle ◽  
Real Vehicle ◽  
Temporal Feature

PurposeIn response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approachThe existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.FindingsFirst, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/valueThe paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

Sign in / Sign up

Export Citation Format

Share Document