Enhanced online convolutional neural networks for object tracking

2018 ◽  
Author(s):  
Dengzhuo Zhang ◽  
Hao Zhou ◽  
Tianwen Li ◽  
Yun GAO
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks

Complementary Object Tracking Using Average Peak-to-Correlation Energy

2021 ◽  
Author(s):  
Kosuke Honda ◽  
Hamido Fujita
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Correlation Energy ◽  
Target Object ◽  
The Other ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Evaluation Index ◽  
Siamese Network

In recent years, template-based methods such as Siamese network trackers and Correlation Filter (CF) based trackers have achieved state-of-the-art performance in several benchmarks. Recent Siamese network trackers use deep features extracted from convolutional neural networks to locate the target. However, the tracking performance of these trackers decreases when there are similar distractors to the object and the target object is deformed. On the other hand, correlation filter (CF)-based trackers that use handcrafted features (e.g., HOG features) to spatially locate the target. These two approaches have complementary characteristics due to differences in learning methods, features used, and the size of search regions. Also, we found that these trackers are complementary in terms of performance in benchmarking. Therefore, we propose the “Complementary Tracking framework using Average peak-to-correlation energy” (CTA). CTA is the generic object tracking framework that connects CF-trackers and Siamese-trackers in parallel and exploits the complementary features of these. In CTA, when a tracking failure of the Siamese tracker is detected using Average peak-to-correlation energy (APCE), which is an evaluation index of the response map matrix, the CF-trackers correct the output. In experimental on OTB100, CTA significantly improves the performance over the original tracker for several combinations of Siamese-trackers and CF-rackers.

Reliability verification-based convolutional neural networks for object tracking

2019 ◽  
Vol 13 (1) ◽  
pp. 175-185 ◽  
Author(s):  
Xiaopeng Hu ◽  
Jingting Li ◽  
Yan Yang ◽  
Fan Wang
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Reliability Verification

Deep convolutional neural networks for thermal infrared object tracking

2017 ◽  
Vol 134 ◽  
pp. 189-198 ◽  
Author(s):  
Qiao Liu ◽  
Xiaohuan Lu ◽  
Zhenyu He ◽  
Chunkai Zhang ◽  
Wen-Sheng Chen
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Thermal Infrared ◽  
Deep Convolutional Neural Networks
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