scholarly journals Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Suryo Adhi Wibowo ◽  
Hansoo Lee ◽  
Eun Kyeong Kim ◽  
Sungshin Kim
Keyword(s):  
Object Tracking ◽  
Target Object ◽  
Visual Object ◽  
Feature Descriptor ◽  
Histogram Of Oriented Gradients ◽  
Visual Object Tracking ◽  
Maximum Value ◽  
Model Update ◽  
Feature Based ◽  
Final Response

Histogram of oriented gradients (HOG) is a feature descriptor typically used for object detection. For object tracking, this feature has certain drawbacks when the target object is influenced by a change in motion or size. In this paper, the use of convolutional shallow features is proposed to improve the performance of HOG feature-based object tracking. Because the proposed method works based on a correlation filter, the response maps for each feature are summed in order to obtain the final response map. The location of the target object is then predicted based on the maximum value of the optimized final response map. Further, a model update is used to overcome the change in appearance of the target object during tracking. A performance evaluation of the proposed method is obtained by using Visual Object Tracking 2015 (VOT2015) benchmark dataset and its protocols. The results are then provided based on their accuracy-robustness (AR) rank. Furthermore, through a comparison with several state-of-the-art tracking algorithms, the proposed method was shown to achieve the highest rank in terms of accuracy and a third rank for robustness. In addition, the proposed method significantly improves the robustness of HOG-based features.

Visual Object Tracking Using Fast Three-Dimensional Discrete Cosine Transform

2014 ◽  
Vol 602-605 ◽  
pp. 1689-1692
Author(s):  
Cong Lin ◽  
Chi Man Pun
Keyword(s):  
Object Tracking ◽  
Discrete Cosine Transform ◽  
Three Dimensional ◽  
Video Stream ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Cosine Transform ◽  
Color Video ◽  
Real Time Application

A novel visual object tracking method for color video stream based on traditional particle filter is proposed in this paper. Feature vectors are extracted from coefficient matrices of fast three-dimensional Discrete Cosine Transform (fast 3-D DCT). The feature, as experiment showed, is very robust to occlusion and rotation and it is not sensitive to scale changes. The proposed method is efficient enough to be used in a real-time application. The experiment was carried out on some common used datasets in literature. The results are satisfied and showed the estimated trace follows the target object very closely.

scholarly journals Distractor-Aware Deep Regression for Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 387 ◽  
Author(s):  
Ming Du ◽  
Yan Ding ◽  
Xiuyun Meng ◽  
Hua-Liang Wei ◽  
Yifan Zhao
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Test Data ◽  
Loss Function ◽  
State Of The Art ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Training Samples ◽  
Better Than

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

scholarly journals Re2EMA: Regularized and Reinitialized Exponential Moving Average for Target Model Update in Object Tracking

2019 ◽  
Vol 33 ◽  
pp. 8457-8464
Author(s):  
Jianglei Huang ◽  
Wengang Zhou
Keyword(s):  
Object Tracking ◽  
Moving Average ◽  
Transformation Matrix ◽  
Visual Object ◽  
Optimal Model ◽  
Visual Object Tracking ◽  
Target Model ◽  
Model Update ◽  
Optimal Target

Target model update plays an important role in visual object tracking. However, performing optimal model update is challenging. In this work, we propose to achieve an optimal target model by learning a transformation matrix from the last target model to the newly generated one, which results into a minimization objective. In this objective, there exists two challenges. The first is that the newly generated target model is unreliable. To overcome this problem, we propose to impose a penalty to limit the distance between the learned target model and the last one. The second is that as time evolves, we can not decide whether the last target model has been corrupted or not. To get out of this dilemma, we propose a reinitialization term. Besides, to control the complexity of the transformation matrix, we also add a regularizer. We find that the optimization formula’s solution, with some simplifications, degenerates to EMA. Finally, despite the simplicity, extensive experiments conducted on several commonly used benchmarks demonstrate the effectiveness of our proposed approach in relatively long term scenarios.

scholarly journals Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3513 ◽  
Author(s):  
Gang-Joon Yoon ◽  
Hyeong Hwang ◽  
Sang Yoon
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Intelligent Systems ◽  
Principal Component ◽  
Research Area ◽  
Target Object ◽  
Visual Object ◽  
Appearance Modeling ◽  
Sparse Principal Component Analysis ◽  
Visual Object Tracking

Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences.

scholarly journals Visual Object Tracking with Online Updating for Car Sharing Services

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhou Zhu ◽  
Haifeng Zhao ◽  
Fang Hui ◽  
Yan Zhang
Keyword(s):  
Object Tracking ◽  
Visual Object ◽  
Car Sharing ◽  
Current Frame ◽  
Visual Object Tracking ◽  
Fixed Weight ◽  
Proposed Model ◽  
Model Update ◽  
Image Pairs ◽  
Update Strategy

In this paper, we address the problem of online updating of visual object tracker for car sharing services. The key idea is to adjust the updating rate adaptively according to the tracking performance of the current frame. Instead of setting a fixed weight for all the frames in the updating of the object model, we assign the current frame a larger weight if its corresponding tracking result is relatively accurate and unbroken and a smaller weight on the contrary. To implement it, the current estimated bounding box’s intersection over union (IOU) is calculated by an IOU predictor which is trained offline on a large number of image pairs and used as a guidance to adjust the updating weights online. Finally, we imbed the proposed model update strategy in a lightweight baseline tracker. Experiment results on both traffic and nontraffic datasets verify that though the error of predicted IOU is inevitable, the proposed method can still improve the accuracy of object tracking compared with the baseline object tracker.

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.

High speed long-term visual object tracking algorithm for real robot systems

2021 ◽  
Vol 434 ◽  
pp. 268-284
Author(s):  
Muxi Jiang ◽  
Rui Li ◽  
Qisheng Liu ◽  
Yingjing Shi ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Robot Systems ◽  
Real Robot
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