scholarly journals Appearance modeling for persistent object tracking in wide-area and full motion video

2016 ◽  
Author(s):  
◽  
Rengarajan Pelapur
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Target Tracking ◽  
Computational Cost ◽  
Wide Area ◽  
Appearance Model ◽  
Urban Scenes ◽  
Update Strategy ◽  
Track Before Detect ◽  
Motion Video

Object tracking is a core element of computer vision and autonomous systems. As such single and multiple object tracking has been widely investigated especially for full motion video sequences. The acquisition of wide-area motion imagery (WAMI) from moving airborne platforms is a much more recent sensor innovation that has an array of defense and civilian applications with numerous opportunities for providing a unique combination of dense spatial and temporal coverage unmatched by other sensor systems. Airborne WAMI presents a host of challenges for object tracking including large data volume, multi-camera arrays, image stabilization, low resolution targets, target appearance variability and high background clutter especially in urban environments. Time varying low frame rate large imagery poses a range of difficulties in terms of reliable long term multi-target tracking. The focus of this thesis is on the Likelihood of Features Tracking (LOFT) testbed system that is an appearance based (single instance) object tracker designed specifcally for WAMI and follows the track before detect paradigm. The motivation for tracking using dynamics before detecting was so that large scale data can be handled in an environment where computational cost can be kept at a bare minimum. Searching for an object everywhere on a large frame is not practical as there are many similar objects, clutter, high rise structures in case of urban scenes and comes with the additional burden of greatly increased computational cost. LOFT bypasses this difficulty by using filtering and dynamics to constrain the search area to a more realistic region within the large frame and uses multiple features to discern objects of interest. The objects of interest are expected as input in the form of bounding boxes to the algorithm. The main goal of this work is to present an appearance update modeling strategy that fits LOFT's track before detect paradigm and to showcase the accuracy of the overall system as compared with other state of the art tracking algorithms and also with and without the presence of this strategy. The update strategy using various information cues from the Radon Transform was designed with certain performance parameters in mind such as minimal increase in computational cost and a considerable increase in precision and recall rates of the overall system. This has been demonstrated with supporting performance numbers using standard evaluation techniques as in literature. The extensions of LOFT WAMI tracker to include a more detailed appearance model with an update strategy that is well suited for persistent target tracking is novel in the opinion of the author. Key engineering contributions have been made with the help of this work wherein the core LOFT has been evaluated as part several government research and development programs including the Air Force Research Lab's Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) Enterprise to the Edge (CETE), Army Research Lab's Advanced Video Activity Analytics (AVAA) and a proposed fine grained distributed computing architecture on the cloud for processing at the edge. A simplified version of LOFT was developed for tracking objects in standard videos and entered in the Visual Object Tracking (VOT) Challenge competition that is held in conjunction with the leading computer vision conferences. LOFT incorporating the proposed appearance adaptation module produces significantly better tracking results in aerial WAMI of urban scenes.

scholarly journals Distributed hybrid consensus–based square-root cubature quadrature information filter and its application to maneuvering target tracking

2019 ◽  
Vol 15 (12) ◽  
pp. 155014771989595
Author(s):  
Jun Liu ◽  
Yu Liu ◽  
Kai Dong ◽  
Ziran Ding ◽  
You He ◽  
...  
Keyword(s):  
Target Tracking ◽  
Nonlinear Filtering ◽  
Computational Cost ◽  
Quadrature Rule ◽  
Square Root ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Matrix Operations ◽  
Information Filter ◽  
Update Strategy

To handle nonlinear filtering problems with networked sensors in a distributed manner, a novel distributed hybrid consensus–based square-root cubature quadrature information filter is proposed. The proposed hybrid consensus–based square-root cubature quadrature information filter exploits fifth-order spherical simplex-radial quadrature rule to tackle system nonlinearities and incorporates a novel measurement update strategy into the hybrid consensus filtering framework, which takes the predicted measurement error into account and hence produces more accurate estimates. In addition, the proposed hybrid consensus–based square-root cubature quadrature information filter inherits the complementary positive features of both consensus on information and consensus on measurements methods and avoids sensitive matrix operations such as square-root decompositions and inversion of covariances, which is beneficial for numerical stability. Stability analysis with respect to consensus, convergence, and consistency for the proposed hybrid consensus–based square-root cubature quadrature information filter is also developed. The effectiveness of the proposed hybrid consensus–based square-root cubature quadrature information filter is validated through a maneuvering target tracking scenario. The simulation results show that the proposed hybrid consensus–based square-root cubature quadrature information filter outperforms the existing algorithms at the expense of a slight increase in computational cost.

Adaptive Image Feature Reduction for Object Tracking

2014 ◽  
Vol 989-994 ◽  
pp. 3605-3608
Author(s):  
Cong Lin ◽  
Chi Man Pun
Keyword(s):  
Object Tracking ◽  
Adaptive Learning ◽  
Computational Cost ◽  
Texture Feature ◽  
Feature Reduction ◽  
Image Feature ◽  
Distribution Model ◽  
Appearance Model ◽  
Art Object

A novel adaptive image feature reduction approach for object tracking using vectorized texture feature is proposed in this paper. Our contributions are three-fold: 1) a statistical discriminative appearance model using texture feature was proposed. 2) Majority of dimensions of the features are removed by judging their errors of the chosen distribution model. The remaining dimensions are most discriminative ones for classification task. The dimension reduction has advantages of reducing the computational cost in classification stage. 3) An adaptive learning rate was proposed to handle drifts caused by long term occlusion. Preliminary experimental results are satisfactory and compared to state-of-the-art object tracking methods.

Effective appearance model update strategy in object tracking

2019 ◽  
Vol 13 (6) ◽  
pp. 531-541 ◽  
Author(s):  
Xianglei Yin ◽  
Guixi Liu
Keyword(s):  
Object Tracking ◽  
Appearance Model ◽  
Model Update ◽  
Update Strategy

Research on Mean Shift Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 4021-4025 ◽  
Author(s):  
Yi Zhi Zhao ◽  
Huan Wang ◽  
Guo Cai Yin
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Target Tracking ◽  
Mean Shift ◽  
Moving Target ◽  
Object Model ◽  
Probability Density Estimation ◽  
Complete Occlusion ◽  
Mean Shift Algorithm ◽  
High Level

Computer vision is a diverse and relatively new field of study. Object tracking plays a crucial role as a preliminary step for high-level image processing in the field of computer vision. However, mean shift algorithm in the target tracking has some defects, such as: the application of fixed bandwidth for probability density estimation usually causes lack of smooth or too smooth; moving target often appears partial occlusion or complete occlusion due to the complexity of the background; background pixels in object model will induce localization error of object tracking, and so on. Therefore, this paper elaborates several elegant algorithms to solve some of the problems. After discussing the application of Mean shift in the field of target tracking, this paper presented an improved Mean shift algorithm by combining Mean Shift and Kalman Filter.

scholarly journals A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

2021 ◽  
Vol 13 (22) ◽  
pp. 4672
Author(s):  
Yinqiang Su ◽  
Jinghong Liu ◽  
Fang Xu ◽  
Xueming Zhang ◽  
Yujia Zuo
Keyword(s):  
Object Tracking ◽  
High Efficiency ◽  
Computational Cost ◽  
Boundary Effects ◽  
Correlation Filter ◽  
Appearance Model ◽  
Temporal Context ◽  
Visual Object ◽  
Context Aware ◽  
Tracking Condition

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APEC-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.

scholarly journals Self-balance Motion and Appearance Model for Multi-object Tracking in UAV

2019 ◽  
Author(s):  
Hongyang Yu ◽  
Guorong Li ◽  
Weigang Zhang ◽  
Hongxun Yao ◽  
Qingming Huang
Keyword(s):  
Object Tracking ◽  
Appearance Model

scholarly journals Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters

2021 ◽  
Author(s):  
Tianyang Xu ◽  
Zhenhua Feng ◽  
Xiao-Jun Wu ◽  
Josef Kittler
Keyword(s):  
Object Tracking ◽  
Augmented Lagrangian Method ◽  
Channel Selection ◽  
Image Feature ◽  
Superior Performance ◽  
Appearance Model ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking ◽  
Feature Representations

AbstractDiscriminative Correlation Filters (DCF) have been shown to achieve impressive performance in visual object tracking. However, existing DCF-based trackers rely heavily on learning regularised appearance models from invariant image feature representations. To further improve the performance of DCF in accuracy and provide a parsimonious model from the attribute perspective, we propose to gauge the relevance of multi-channel features for the purpose of channel selection. This is achieved by assessing the information conveyed by the features of each channel as a group, using an adaptive group elastic net inducing independent sparsity and temporal smoothness on the DCF solution. The robustness and stability of the learned appearance model are significantly enhanced by the proposed method as the process of channel selection performs implicit spatial regularisation. We use the augmented Lagrangian method to optimise the discriminative filters efficiently. The experimental results obtained on a number of well-known benchmarking datasets demonstrate the effectiveness and stability of the proposed method. A superior performance over the state-of-the-art trackers is achieved using less than $$10\%$$ 10 % deep feature channels.

Dual-scale structural local sparse appearance model for robust object tracking

2017 ◽  
Vol 237 ◽  
pp. 101-113 ◽  
Author(s):  
Zhiqiang Zhao ◽  
Ping Feng ◽  
Tianjiang Wang ◽  
Fang Liu ◽  
Caihong Yuan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Appearance Model ◽  
Dual Scale
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