scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

A Robust Visual Tracker Based on DCF Algorithm

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1819-1834
Author(s):  
Menglei Jin ◽  
Weibin Liu ◽  
Weiwei Xing
Keyword(s):  
Linear Interpolation ◽  
Correlation Filter ◽  
Background Information ◽  
Video Object Tracking ◽  
Multiple Datasets ◽  
Tracking Algorithms ◽  
Current Correlation ◽  
Model Update ◽  
Global Background ◽  
Update Strategy

Since Correlation Filter appeared in the field of video object tracking, it is very popular due to its excellent performance. The Correlation Filter-based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness. However, there are still some fields for improvement in the Correlation Filter-based tracking algorithms. First, during the training of the classifier, the background information that can be utilized is very limited. Moreover, the introduction of the cosine window further reduces the background information. These reasons reduce the discriminating power of the classifier. This paper introduces more global background information on the basis of the DCF tracker to improve the discriminating ability of the classifier. Then, in some complex scenes, tracking loss is easy to occur. At this point, the tracker will be treated the background information as the object. To solve this problem, this paper introduces a novel re-detection component. Finally, the current Correlation Filter-based tracking algorithms use the linear interpolation model update method, which cannot adapt to the object changes in time. This paper proposes an adaptive model update strategy to improve the robustness of the tracker. The experimental results on multiple datasets can show that the tracking algorithm proposed in this paper is an excellent algorithm.

scholarly journals Model Update Strategies about Object Tracking: A State of the Art Review

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1207 ◽  
Author(s):  
Wang ◽  
Fang ◽  
Chen ◽  
Sun ◽  
Chen
Keyword(s):  
Object Tracking ◽  
Target Location ◽  
State Of The Art ◽  
Crucial Point ◽  
Target Model ◽  
Model Update ◽  
Tracking Model ◽  
Update Strategy ◽  
Specific Sequences

Object tracking has always been an interesting and essential research topic in the domain of computer vision, of which the model update mechanism is an essential work, therefore the robustness of it has become a crucial factor influencing the quality of tracking of a sequence. This review analyses on recent tracking model update strategies, where target model update occasion is first discussed, then we give a detailed discussion on update strategies of the target model based on the mainstream tracking frameworks, and the background update frameworks are discussed afterwards. The experimental performances of the trackers in recent researches acting on specific sequences are listed in this review, where the superiority and some failure cases on each of them are discussed, and conclusions based on those performances are then drawn. It is a crucial point that design of a proper background model as well as its update strategy ought to be put into consideration. A cascade update of the template corresponding to each deep network layer based on the contributions of them to the target recognition can also help with more accurate target location, where target saliency information can be utilized as a tool for state estimation.

scholarly journals An adaptive eco with weighted feature for visual tracking

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5139-5148
Author(s):  
Yan Zhou ◽  
Hongwei Guo ◽  
Dongli Wang ◽  
Chunjiang Liao
Keyword(s):  
Fuzzy Model ◽  
Visual Object ◽  
Adaptive Mechanism ◽  
Feature Maps ◽  
Visual Object Tracking ◽  
Tracking Process ◽  
Model Update ◽  
Fitting In ◽  
Update Strategy ◽  
Self Adaptive

The efficient convolution operator (ECO) have manifested predominant results in visual object tracking. However, in the pursuit of performance improvement, the computational burden of the tracker becomes heavy, and the importance of different feature layers is not considered. In this paper, we propose a self-adaptive mechanism for regulating the training process in the first frame. To overcome the over-fitting in the tracking process, we adopt the fuzzy model update strategy. Moreover, we weight different feature maps to enhance the tracker performance. Comprehensive experiments have conducted on the OTB-2013 dataset. When adopting our ideas to adjust our tracker, the self-adaptive mechanism can avoid unnecessary training iterations, and the fuzzy update strategy reduces one fifth tracking computation compared to the ECO. Within reduced computation, the tracker based our idea incurs less than 1% loss in AUC (area-undercurve).

scholarly journals Adaptive Model Update Strategy for Correlation Filter Trackers

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 151493-151505
Author(s):  
Zhuang He ◽  
Qi Li ◽  
Meng Chang ◽  
Huajun Feng ◽  
Zhihai Xu
Keyword(s):  
Correlation Filter ◽  
Adaptive Model ◽  
Model Update ◽  
Update Strategy

scholarly journals Long-Term Target Tracking of UAVs Based on Kernelized Correlation Filter

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3006
Author(s):  
Junqiang Yang ◽  
Wenbing Tang ◽  
Zuohua Ding
Keyword(s):  
Target Tracking ◽  
Template Matching ◽  
Control Method ◽  
Target Position ◽  
Correlation Filter ◽  
Background Information ◽  
The Status ◽  
Tracking Model ◽  
Update Strategy

During the target tracking process of unmanned aerial vehicles (UAVs), the target may disappear from view or be fully occluded by other objects, resulting in tracking failure. Therefore, determining how to identify tracking failure and re-detect the target is the key to the long-term target tracking of UAVs. Kernelized correlation filter (KCF) has been very popular for its satisfactory speed and accuracy since it was proposed. It is very suitable for UAV target tracking systems with high real-time requirements. However, it cannot detect tracking failure, so it is not suitable for long-term target tracking. Based on the above research, we propose an improved KCF to match long-term target tracking requirements. Firstly, we introduce a confidence mechanism to evaluate the target tracking results to determine the status of target tracking. Secondly, the tracking model update strategy is designed to make the model suffer from less background information interference, thereby improving the robustness of the algorithm. Finally, the Normalized Cross Correlation (NCC) template matching is used to make a regional proposal first, and then the tracking model is used for target re-detection. Then, we successfully apply the algorithm to the UAV system. The system uses binocular cameras to estimate the target position accurately, and we design a control method to keep the target in the UAV’s field of view. Our algorithm has achieved the best results in both short-term and long-term evaluations of experiments on tracking benchmarks, which proves that the algorithm is superior to the baseline algorithm and has quite good performance. Outdoor experiments show that the developed UAV system can achieve long-term, autonomous target tracking.

scholarly journals An Improved Kernelized Correlation Filter Based Visual Tracking Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianjun Ni ◽  
Xue Zhang ◽  
Pengfei Shi ◽  
Jinxiu Zhu
Keyword(s):  
Visual Tracking ◽  
Template Matching ◽  
Visual Target ◽  
State Of The Art ◽  
Correlation Filter ◽  
Matching Method ◽  
Tracking Method ◽  
Template Update ◽  
Update Strategy ◽  
Visual Target Tracking

Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.

scholarly journals PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Thermal Power Plants ◽  
Average Precision ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

scholarly journals EfficientPS: Efficient Panoptic Segmentation

2021 ◽  
Author(s):  
Rohit Mohan ◽  
Abhinav Valada
Keyword(s):  
State Of The Art ◽  
Autonomous Robot ◽  
Multi Scale ◽  
Contextual Features ◽  
New Variant ◽  
Competent Functioning ◽  
Segmentation Task

AbstractUnderstanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

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.

Sign in / Sign up

Export Citation Format

Share Document