Learning region sparse constraint correlation filter for tracking

2021 ◽  
Vol 90 ◽  
pp. 116042
Author(s):  
Zheng Peng ◽  
XinJiang Lu
Keyword(s):  
Correlation Filter ◽  
Sparse Constraint ◽  
Learning Region

Multi-template Correlation Filter Tracking Based on Deep Feature

2019 ◽  
Vol 31 (5) ◽  
pp. 792
Author(s):  
Zongmin Li ◽  
Hongjiao Fu ◽  
Yujie Liu ◽  
Hua Li
Keyword(s):  
Correlation Filter ◽  
Deep Feature

Learning spatio-temporal correlation filter for visual tracking

2021 ◽  
Vol 436 ◽  
pp. 273-282
Author(s):  
Youmin Yan ◽  
Xixian Guo ◽  
Jin Tang ◽  
Chenglong Li ◽  
Xin Wang
Keyword(s):  
Visual Tracking ◽  
Temporal Correlation ◽  
Correlation Filter ◽  
Spatio Temporal

scholarly journals Learning Region-Based Attention Network for Traffic Sign Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 686
Author(s):  
Ke Zhou ◽  
Yufei Zhan ◽  
Dongmei Fu
Keyword(s):  
High Resolution ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Attention Network ◽  
Sign Recognition ◽  
Learning Region ◽  
Robust Network ◽  
Traffic Sign Classification

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).

Rotation adaptive correlation filter for moving object tracking in satellite videos

2021 ◽  
Vol 438 ◽  
pp. 94-106
Author(s):  
Shiyu Xuan ◽  
Shengyang Li ◽  
Zifei Zhao ◽  
Zhuang Zhou ◽  
Wanfeng Zhang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Moving Object ◽  
Correlation Filter ◽  
Moving Object Tracking

scholarly journals Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2841
Author(s):  
Khizer Mehmood ◽  
Abdul Jalil ◽  
Ahmad Ali ◽  
Baber Khan ◽  
Maria Murad ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Environmental Changes ◽  
Correlation Filter ◽  
Temporal Context ◽  
Visual Object ◽  
Target Model ◽  
Change Of Scale ◽  
Tracking Model ◽  
Spatio Temporal

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

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