scholarly journals Extended occupation grids for non-rigid moving objects tracking

2011 ◽  
Author(s):  
Benjamin Lefaudeux ◽  
Gwennael Gate ◽  
Fawzi Nashashibi
Keyword(s):  
Moving Objects ◽  
Objects Tracking

Real-Time Moving Objects Tracking for Distributed Smart Video Surveillances

2016 ◽  
Vol 11 (4) ◽  
pp. 324
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Objects Tracking

Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinchao Huang
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Motion Blur ◽  
Feature Maps ◽  
Content Type ◽  
Objects Tracking ◽  
Tracking Model ◽  
Background Motion ◽  
Gated Recurrent Unit ◽  
Attentional Mechanism

PurposeMulti-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approachFirst, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.FindingsIn order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/valueThis paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.

Moving Objects Tracking

2014 ◽  
pp. 33-50
Author(s):  
Xiaofeng Meng ◽  
Zhiming Ding ◽  
Jiajie Xu
Keyword(s):  
Moving Objects ◽  
Objects Tracking

Occlusion Handling Method for Multiple Moving Objects Tracking Based on Multilevel Tracker Queues

2011 ◽  
Vol 31 (8) ◽  
pp. 0815001
Author(s):  
金标 Jin Biao ◽  
胡文龙 Hu Wenlong ◽  
王宏琦 Wang Hongqi
Keyword(s):  
Moving Objects ◽  
Occlusion Handling ◽  
Objects Tracking ◽  
Handling Method
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