Real-Time Probabilistic Covariance Tracking With Efficient Model Update

2012 ◽  
Vol 21 (5) ◽  
pp. 2824-2837 ◽  
Author(s):  
Yi Wu ◽  
Jian Cheng ◽  
Jinqiao Wang ◽  
Hanqing Lu ◽  
Jun Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Model Update ◽  
Covariance Tracking

scholarly journals Real-Time Thermal Infrared Tracking Based on Collaborative Online and Offline Method

2018 ◽  
Vol 36 (6) ◽  
pp. 1052-1058 ◽  
Author(s):  
Ximing Zhang ◽  
Mingang Wang ◽  
Lin Cao
Keyword(s):  
Real Time ◽  
Visual Cues ◽  
Detection Method ◽  
State Of The Art ◽  
Thermal Infrared ◽  
Superior Performance ◽  
Model Update ◽  
Real Time Tracking ◽  
Siamese Networks ◽  
Infrared Tracking

Most tracking-by-detection based trackers employ the online model update scheme based on the spatiotemporal consistency of visual cues. In presence of self-deformation, abrupt motion and heavy occlusion, these trackers suffer from different attributes and are prone to drifting. The model based on offline training, namely Siamese networks is invariant when suffering from the attributes. While the tracking speed of the offline method can be slow which is not enough for real-time tracking. In this paper, a novel collaborative tracker which decomposes the tracking task into online and offline modes is proposed. Our tracker switches between the online and offline modes automatically based on the tracker status inferred from the present failure tracking detection method which is based on the dispersal measure of the response map. The present Real-Time Thermal Infrared Collaborative Online and Offline Tracker (TCOOT) achieves state-of-the-art tracking performance while maintaining real-time speed at the same time. Experiments are carried out on the VOT-TIR-2015 benchmark dataset and our tracker achieves superior performance against Staple and Siam FC trackers by 3.3% and 3.6% on precision criterion and 3.8% and 5% on success criterion, respectively. The present method is real-time tracker as well.

scholarly journals rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking

2020 ◽  
Vol 10 (9) ◽  
pp. 3021
Author(s):  
Wangpeng He ◽  
Heyi Li ◽  
Wei Liu ◽  
Cheng Li ◽  
Baolong Guo
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Target Position ◽  
Color Histogram ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Training Samples ◽  
Model Update ◽  
Score Map ◽  
Scale Detection

Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed “robust sum of template and pixel-wise learners” (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.

Interconnection Model Update in Real-Time

1979 ◽  
Vol 12 (5) ◽  
pp. 150-153
Author(s):  
Kavuru A. Ramarao
Keyword(s):  
Real Time ◽  
Model Update

scholarly journals SHM and Efficient Strategies for Reduced-Order Modeling

2021 ◽  
Vol 2 (1) ◽  
pp. 98
Author(s):  
Giorgio Gobat ◽  
Saeed Eftekhar Azam ◽  
Stefano Mariani
Keyword(s):  
Real Time ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Order Model ◽  
Model Order ◽  
Reduced Order ◽  
Degradation Processes ◽  
Model Update ◽  
Real Time Computing ◽  
Proper Orthogonal

Within model-based approaches to structural health monitoring (SHM), numerical simulations must be tailored to continuously adapt to the degradation processes and to the possibly changing environment. This model update stage of the analysis brings two competing requirements: the accuracy of the model, with a more detailed description of the phenomena required where damage is supposed to take place; the efficiency of the model, to reduce the overall computational burden and allow for real-time (or close to real-time) computing. Without resorting to AI-based strategies, approaches solely based on proper orthogonal decomposition (POD) and domain decomposition (DD) techniques proved rather efficient in handling the aforementioned trade-off between the diverging requirements of accuracy and efficiency. In this work, we discuss a further improvement over our recently proposed methodology that consists of: a DD of the entire structure into sub-regions, which can be designed to decouple regions more prone to get damaged from regions that are instead less affected by the degradation processes; a POD-based selective model order reduction for all the domains, with adjustable and heterogeneous accuracy requirements. The approach is assessed through an illustrative example related to beam dynamics, with results provided in terms of both accuracy and computational efficiency, or speedup with respect to the full-order model.

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