Moving target detection in complex scenes based on spatio-temporal domain analysis

2010 ◽  
Author(s):  
Xiaowei Zhang ◽  
Jianxiong Zhou
Keyword(s):  
Target Detection ◽  
Domain Analysis ◽  
Moving Target ◽  
Moving Target Detection ◽  
Temporal Domain ◽  
Complex Scenes ◽  
Spatio Temporal

Dim moving target detection algorithm based on spatio-temporal classification sparse representation

2014 ◽  
Vol 67 ◽  
pp. 273-282 ◽  
Author(s):  
Zhengzhou Li ◽  
Zhen Dai ◽  
Hongxia Fu ◽  
Qian Hou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal

Moving target detection approach based on spatio-temporal salient perception

Optik ◽  
2014 ◽  
Vol 125 (22) ◽  
pp. 6681-6686 ◽  
Author(s):  
Gang Jin ◽  
Zhengzhou Li ◽  
Yuanshan Gu ◽  
Jialing Li ◽  
Dong Cao ◽  
...  
Keyword(s):  
Target Detection ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection Approach ◽  
Spatio Temporal

scholarly journals A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences

2020 ◽  
Vol 12 (17) ◽  
pp. 2783
Author(s):  
Zhaoxu Li ◽  
Qiang Ling ◽  
Jing Wu ◽  
Zhengyan Wang ◽  
Zaiping Lin
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Spectral Information ◽  
Moving Target ◽  
Complex Motion ◽  
Multiple Targets ◽  
Moving Target Detection ◽  
Spatio Temporal

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.

A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences

2016 ◽  
Vol 369 ◽  
pp. 548-563 ◽  
Author(s):  
Yansheng Li ◽  
Yongjun Zhang ◽  
Jin-Gang Yu ◽  
Yihua Tan ◽  
Jinwen Tian ◽  
...  
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Image Sequences ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal

Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

2015 ◽  
Vol 69 ◽  
pp. 44-52 ◽  
Author(s):  
Zhengzhou Li ◽  
Qian Hou ◽  
Hongxia Fu ◽  
Zhen Dai ◽  
Lijiao Yang ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Sparse Recovery ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal
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