Multi-scale spatio-temporal context visual tracking algorithm based on target model adaptive update

2020 ◽  
Author(s):  
Faling Chen ◽  
Qinghai Ding ◽  
Haibo Luo ◽  
Bin Hui ◽  
Zheng Chang ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm ◽  
Temporal Context ◽  
Target Model ◽  
Multi Scale ◽  
Spatio Temporal

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.

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

scholarly journals Multi-Channel Features Spatio-Temporal Context Learning for Visual Tracking

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12856-12864 ◽  
Author(s):  
Xiaoqin Zhou ◽  
Xiaofeng Liu ◽  
Chenguang Yang ◽  
Aimin Jiang ◽  
Bin Yan
Keyword(s):  
Visual Tracking ◽  
Temporal Context ◽  
Context Learning ◽  
Spatio Temporal

scholarly journals Orderless and Blurred Visual Tracking via Spatio-temporal Context

2015 ◽  
pp. 25-36 ◽  
Author(s):  
Manna Dai ◽  
Peijie Lin ◽  
Lijun Wu ◽  
Zhicong Chen ◽  
Songlin Lai ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Temporal Context ◽  
Spatio Temporal
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