Unsupervised spatio-temporal segmentation for extracting moving objects in video sequences

2009 ◽  
Vol 14 (2) ◽  
pp. 154-161 ◽  
Author(s):  
Ren-jie Li ◽  
Song-yu Yu ◽  
Xiang-wen Wang
Keyword(s):  
Moving Objects ◽  
Video Sequences ◽  
Temporal Segmentation ◽  
Spatio Temporal

Spatio–temporal Segmentation Using Laserscanner and Video Sequences

2004 ◽  
pp. 367-374 ◽  
Author(s):  
Nico Kaempchen ◽  
Markus Zocholl ◽  
Klaus C. J. Dietmayer
Keyword(s):  
Video Sequences ◽  
Temporal Segmentation ◽  
Spatio Temporal

Active video flow control technique based on spatio-temporal segmentation of video sequences

2001 ◽  
Author(s):  
Cristina Urdiales ◽  
Antonio J. Bandera ◽  
Juan A. Rodriguez ◽  
Francisco Sandoval
Keyword(s):  
Flow Control ◽  
Control Technique ◽  
Video Sequences ◽  
Temporal Segmentation ◽  
Active Video ◽  
Spatio Temporal

Spatio-temporal segmentation of moving objects using edge features in infrared videos

Optik ◽  
2014 ◽  
Vol 125 (7) ◽  
pp. 1809-1815 ◽  
Author(s):  
Chaobo Min ◽  
Junju Zhang ◽  
Benkang Chang ◽  
Bin Sun ◽  
Yingjie Li
Keyword(s):  
Moving Objects ◽  
Temporal Segmentation ◽  
Spatio Temporal ◽  
Edge Features

A Spatio-Temporal Video Segmentation Method Based on Motion Detection

2011 ◽  
Vol 135-136 ◽  
pp. 1147-1154
Author(s):  
Jin Wang ◽  
Zhao Hui Li ◽  
Dong Mei Li ◽  
Yu Wang
Keyword(s):  
Motion Detection ◽  
Moving Objects ◽  
Video Segmentation ◽  
High Threshold ◽  
Video Object ◽  
Temporal Segmentation ◽  
Canny Operator ◽  
Temporal Video Segmentation ◽  
Temporal And Spatial ◽  
Spatio Temporal

In the paper, a new spatio-temporal segmentation algorithm is proposed to extract moving objects from video sequences, the sequences were taken by stationary cameras. First, the motion detection is used to achieve the mask representing moving regions with a estimation noise parameter. Which can effectively improve noise immunity. Due to the shortage of the moving video object textures, the eight-neighbor motion detection is present, which is used to smooth the mask boundary and fill the interior holes. Then a morphological filter is applied to refine the moving mask. Second, spatial segmentation is detected by the Canny operator. Then utilize the gradient histogram to select the high threshold to increase the adaptivity of Canny algorithm. Finally, merge the temporal and spatial mask by neighborhood matching algorithm to ensure further reliability and efficiency of our algorithm. Experiments on typical sequences have successfully demonstrated the validity of the proposed algorithm.

scholarly journals Spatio-temporal Segmentation Based Adaptive Compression of Dynamic Mesh Sequences

2020 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Guoliang Luo ◽  
Zhigang Deng ◽  
Xin Zhao ◽  
Xiaogang Jin ◽  
Wei Zeng ◽  
...  
Keyword(s):  
Dynamic Mesh ◽  
Temporal Segmentation ◽  
Adaptive Compression ◽  
Spatio Temporal

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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