Adaptive Image Feature Reduction for Object Tracking

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.3605 ◽

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.

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

International Journal of Computer Vision ◽

10.1007/s11263-021-01435-1 ◽

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.

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

10.32469/10355/57252 ◽

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.

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Parallel tracking and detection for long-term object tracking

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420902577 ◽

2020 ◽

Vol 17 (2) ◽

pp. 172988142090257

Author(s):

Dan Xiong ◽

Huimin Lu ◽

Qinghua Yu ◽

Junhao Xiao ◽

Wei Han ◽

...

Keyword(s):

Object Detection ◽

Object Tracking ◽

Detection Algorithm ◽

Detection Algorithms ◽

Art Object ◽

High Processing Speed ◽

Online Learning Algorithms ◽

Integrate State ◽

Unknown Object

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

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

Remote Sensing ◽

10.3390/rs13224672 ◽

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.

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Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking

Applied Sciences ◽

10.3390/app8081349 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1349 ◽

Cited By ~ 2

Author(s):

Soowoong Jeong ◽

Joonki Paik

Keyword(s):

Object Tracking ◽

Real Time ◽

Adaptive Learning ◽

Weighting Function ◽

Computational Cost ◽

Visual Object ◽

Partial Occlusion ◽

Block Scheme ◽

Real Time Tracking ◽

Scale Variation

In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers.

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