scholarly journals Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1625 ◽  
Author(s):  
Hao Yang ◽  
Yingqing Huang ◽  
Zhihong Xie
Keyword(s):  
Kalman Filter ◽  
Texture Features ◽  
Boundary Effects ◽  
Correlation Filter ◽  
Target Response ◽  
Correlation Filters ◽  
Adaptive Kalman Filter ◽  
Optimal Functions ◽  
Discriminative Correlation Filter ◽  
High Computational Efficiency

In the field of visual tracking, discriminative correlation filter (DCF)-based trackers have made remarkable achievements with their high computational efficiency. The crucial challenge that still remains is how to construct qualified samples without boundary effects and redetect occluded targets. In this paper a feature-enhanced discriminative correlation filter (FEDCF) tracker is proposed, which utilizes the color statistical model to strengthen the texture features (like the histograms of oriented gradient of HOG) and uses the spatial-prior function to suppress the boundary effects. Then, improved correlation filters using the enhanced features are built, the optimal functions of which can be effectively solved by Gauss–Seidel iteration. In addition, the average peak-response difference (APRD) is proposed to reflect the degree of target-occlusion according to the target response, and an adaptive Kalman filter is established to support the target redetection. The proposed tracker achieved a success plot performance of 67.8% with 5.1 fps on the standard datasets OTB2013.

scholarly journals Learning Spatial–Temporal Background-Aware Based Tracking

2021 ◽  
Vol 11 (18) ◽  
pp. 8427
Author(s):  
Peiting Gu ◽  
Peizhong Liu ◽  
Jianhua Deng ◽  
Zhi Chen
Keyword(s):  
State Of The Art ◽  
Feature Representation ◽  
Boundary Effects ◽  
Correlation Filter ◽  
Discriminative Performance ◽  
Sampling Process ◽  
Alternating Direction ◽  
Discriminative Correlation Filter ◽  
Speed And Accuracy ◽  
Appearance Changes

Discriminative correlation filter (DCF) based tracking algorithms have obtained prominent speed and accuracy strengths, which have attracted extensive attention and research. However, some unavoidable deficiencies still exist. For example, the circulant shifted sampling process is likely to cause repeated periodic assumptions and cause boundary effects, which degrades the tracker’s discriminative performance, and the target is not easy to locate in complex appearance changes. In this paper, a spatial–temporal regularization module based on BACF (background-aware correlation filter) framework is proposed, which is performed by introducing a temporal regularization to deal effectively with the boundary effects issue. At the same time, the accuracy of target recognition is improved. This model can be effectively optimized by employing the alternating direction multiplier (ADMM) method, and each sub-problem has a corresponding closed solution. In addition, in terms of feature representation, we combine traditional hand-crafted features with deep convolution features linearly enhance the discriminative performance of the filter. Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100.

Discriminative Correlation Filter for Long-Time Tracking

2019 ◽  
Vol 63 (3) ◽  
pp. 460-468
Author(s):  
Faming Gong ◽  
Hanbing Yue ◽  
Xiangbing Yuan ◽  
Wenjuan Gong ◽  
Tao Song
Keyword(s):  
Feature Detection ◽  
Correlation Filter ◽  
Tracking Accuracy ◽  
Correlation Filters ◽  
Scale Invariant ◽  
Long Time ◽  
Discriminative Correlation Filter ◽  
Fast Motion ◽  
Scale Invariant Feature ◽  
Point Detection

Abstract Object tracking is a very important step in building an intelligent video monitoring system that can protect people’s lives and property. In recent years, although visual tracking has made great progress in terms of speed and accuracy, there are still few real-time high-precision tracking algorithms. Although discriminative correlation filters have excellent performance in tracking speed, there are deficiencies in handling fast motion. This leads to the inability to achieve long-term stable tracking results. The long-time tracking with discriminative correlation filter (LT-DCF) was proposed to solve these deficiencies. We use larger size detection image blocks and smaller size filters to increase the proportion of real samples to solve the boundary effects of fast motion. And we combine the histogram of oriented gradient (HOG) feature detection and scale-invariant feature transform (SIFT) key point detection to solve the obstacles caused by scale variations. The detector with deep feature flow is then incorporated into the tracker to detect key frames to improve tracking accuracy. This method has achieved more than 75% of the distance accuracy and 70% of the overlapping success rate on the VOT2015 and VOT2016 datasets, and the stable tracking video length can reach 6895 frames.

scholarly journals Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1129 ◽  
Author(s):  
Jianming Zhang ◽  
Yang Liu ◽  
Hehua Liu ◽  
Jin Wang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Correlation Filter ◽  
Appearance Model ◽  
Visual Object ◽  
Multiple Correlation ◽  
Correlation Filters ◽  
Tracking Algorithms ◽  
Global Correlation ◽  
Object Appearance

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

Kalman Filter For Spatial-temporal Regularized Correlation Filters

2021 ◽  
pp. 1-1
Author(s):  
Sheng Feng ◽  
Keli Hu ◽  
En Fan ◽  
Liping Zhao ◽  
Chengdong Wu
Keyword(s):  
Kalman Filter ◽  
Correlation Filters

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.

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