Improved Object Tracking Research Articles

A Practical Approach to Tracking Estimation Using Object Trajectory Linearization

In the field of image processing and machine vision, object tracking is a significant and rapidly developing subfield. The numerous potential applications of object tracking have garnered much attention in recent years. The effectiveness of tracking and detecting moving targets is directly related to the quality of motion detection algorithms. This paper presents a new method for estimating the tracking of objects by linearizing their trajectories. Estimating the movement paths of objects in dynamic and complex environments is one of the fundamental challenges in various fields, such as surveillance systems, autonomous navigation, and robotics. Existing methods, such as the Kalman filter and particle filter, each have their strengths and weaknesses. The Kalman filter is suitable for linear systems but less efficient in nonlinear systems, while the particle filter can better handle system nonlinearity but requires more computations. The main goal of this research is to improve the accuracy and efficiency of estimating the movement paths of objects by combining path linearization techniques with existing advanced methods. In this method, the nonlinear model of the object's path is first transformed into a simpler linear model using linearization techniques. The Kalman filter is then used to estimate the states of the linearized system. This approach simplifies the calculations while increasing the estimation accuracy. In the subsequent step, a particle filter-based method is employed to manage noise and sudden changes in the object's trajectory. This combination of two different methods allows leveraging the advantages of both, resulting in a more accurate and robust estimate. Experimental results show that the proposed method performs better than traditional methods, achieving higher accuracy in various conditions, including those with high noise and sudden changes in the movement path. Specifically, the proposed approach improves movement forecasting accuracy by about 12% compared to existing methods. In conclusion, this research demonstrates that object trajectory linearization can be an effective tool for improving object tracking estimation. Combining this technique with existing advanced methods can enhance the accuracy and efficiency of tracking systems. Consequently, the results of this research can be applied to the development of advanced surveillance systems, self-driving cars, and other applications.

Analysis of different noise filtering techniques for object detection and tracking from video with varying illumination

In order to improve object tracking and detection in films with different lighting conditions, this paper provides a thorough review of noise filtering methods. Computer vision systems face difficulties due to the variety of illumination conditions found in real-world settings, which calls for effective noise reduction techniques. We explore and evaluate several filtering techniques, such as deep learning-based methods, adaptive algorithms, guided bilateral filter and spatial and temporal filters, to determine how well they preserve object characteristics while cutting down on noise. We simulate real-world conditions with our studies on video datasets that have dynamic illumination changes. The outcomes show how well guided bilateral filter methods work in difficult illumination situations to preserve precise object trajectories and boundaries. To help practitioners choose the best noise filtering algorithms based on application-specific needs, we also go over the computational consequences and trade-offs related to each strategy. This work addresses a crucial component of real-world object detection and tracking in movies with variable lighting, offering insightful new information to the field of computer vision.

Deep Neural Network-based Multi-Object Tracker in Complex Events

Deep learning has been shown to be efficient for multiple object tracking, despite the challenges of frequently occurring occlusions, uncertain appearances, objects in as well as out, and insufficient labeled data. Detecting and tracking objects is one of the most common and difficult jobs that surveillance systems must undertake in order to recognize important events and suspicious conduct, as well as automatically remark and extract video information. The progress of convolutional neural networks (CNN) changes the way objects are tracked. CNN layers trained upon a significant amount of videos or image sequences improve object tracking accuracy in shorter time periods. This study analyses and compares the network model and tracking techniques with its performance measures.

Infrastructure sensor-based cooperative perception for early stage connected and automated vehicle deployment

Infrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results.

TMTNet: A Transformer-Based Multimodality Information Transfer Network for Hyperspectral Object Tracking

Hyperspectral video with spatial and spectral information has great potential to improve object tracking performance. However, the limited hyperspectral training samples hinder the development of hyperspectral object tracking. Since hyperspectral data has multiple bands, from which any three bands can be extracted to form pseudocolor images, we propose a Transformer-based multimodality information transfer network (TMTNet), aiming to improve the tracking performance by efficiently transferring the information of multimodality data composed of RGB and hyperspectral in the hyperspectral tracking process. The multimodality information needed to be transferred mainly includes the RGB and hyperspectral multimodality fusion information and the RGB modality information. Specifically, we construct two subnetworks to transfer the multimodality fusion information and the robust RGB visual information, respectively. Among them, the multimodality fusion information transfer subnetwork is designed based on the dual Siamese branch structure. The subnetwork employs the pretrained RGB tracking model as the RGB branch to guide the training of the hyperspectral branch with little training samples. The RGB modality information transfer subnetwork is designed based on a pretrained RGB tracking model with good performance to improve the tracking network’s generalization and accuracy in unknown complex scenes. In addition, we design an information interaction module based on Transformer in the multimodality fusion information transfer subnetwork. The module can fuse multimodality information by capturing the potential interaction between different modalities. We also add a spatial optimization module to TMTNet, which further optimizes the object position predicted by the subject network by fully retaining and utilizing detailed spatial information. Experimental results on the only available hyperspectral tracking benchmark dataset show that the proposed TMTNet tracker outperforms the advanced trackers, demonstrating the effectiveness of this method.

An Improved Object Tracking Technique for Remote Weapon Station Using Yolov5_Deepsort_Dlib Architecture

An Improved Object Tracking Technique for Remote Weapon Station Using Yolov5_Deepsort_Dlib Architecture

A motional but temporally consistent physical video examples

Adversarial examples (AEs) attract extensive attention due to their inherent security-related properties of attacking Deep Neural Networks (DNNs) through carefully constructed modifications. Recently, they have been extended to video tasks. Human action recognition based on DNNs is a crucial task in video tasks. AEs of human action recognition models attract much focus and previous works demonstrated the vulnerability of AEs of human action recognition in the digital world. However, the adversarial videos for attacking human action recognition models in the physical world are still in the open stage. The design of physical adversarial videos is crucial for helping to evaluate the robustness of some critical applications based on human action recognition, e.g., surveillance and pedestrian detection. Unlike the digital attacks on action recognition models, the perturbations of physical adversarial videos should be motional but temporally consistent across each whole video. The attacks need to destroy the spatial interactions and temporal interactions in videos. Previously developed attacks for video models in the digital world are difficult to transfer to the physical world. In this paper, we close this gap, and we are the first to attack human action recognition models in the physical world. We first generate a dynamic mask via an improved object tracking method and then use the center location to construct a motion location map. Finally, gradient sharing method is used to generate temporally consistent perturbations and optimize the perturbations into robust patches. Experiments show that these patches can successfully attack a real-time human action recognition system, and the proposed approach has a 77.5% success rate in this setting.

An Improved Object Tracking and Estimating Using Adaptive Kalman Filter Based Yolov5 Model and Mixed Precision for Efficient Inference Performance

An Improved Object Tracking and Estimating Using Adaptive Kalman Filter Based Yolov5 Model and Mixed Precision for Efficient Inference Performance

An Improved Object Tracking and Estimating Using Yolov5 Model Based on Adaptive Kalman Filter and Efficient Inference Performance on Surveillance

An Improved Object Tracking and Estimating Using Yolov5 Model Based on Adaptive Kalman Filter and Efficient Inference Performance on Surveillance

Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

PurposeMulti-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approachFirst, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.FindingsIn order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/valueThis paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.

Algorithms and Analysis for Optimizing the Tracking Performance of Cyber Attacked Sensor-Equipped Connected Vehicle Networks

Sensor-equipped connected vehicle networks (SECVNs) have the potential to enable substantially safer driving by improved object tracking, which is an important basic building block in SECVNs. Unfortunately, cyber-attacks on SECVNs pose a very serious threat which could lead to unacceptable outcomes, including fatalities. Recently there has been increasing focus on malicious attack detection and mitigation in SECVNs, and some of this work has considered attacks on sensor data to impact object tracking. Unfortunately, low complexity mitigation approaches which do not compromise performance are lacking. This paper describes an efficient machine-learning enhanced approach for tracking under cyber-attacks. By proper selection of some variances related to the sensor and prior probability density functions, under some assumptions the performance can be made as close as desired to a bound on the best possible performance. However, the complexity of this new approach is dramatically lower than the best existing published low complexity approach, which provides performance which is substantially inferior to that provided by the new approach. The new approach also provides much better scaling with the size of the SECVN. In particular, the complexity increases linearly in the number of sensors, while the best low complexity published approach has a complexity which grows quadratically in the number of sensors. The new approach is also applicable to other tracking applications.

Object tracking in siamese network with attention mechanism and Mish function

In order to improve the recognition and tracking ability of the fully-convolutional siamese networks for object tracking in complex scenes, this paper proposes an improved object tracking algorithm with channel attention mechanism and Mish activation function. First, the channel attention mechanism is introduced into the model, and different weights are assigned to each channel to improve the network’s representation ability. At the same time, the Mish function is used to replace the ReLU activation function in the network. The smooth Mish function can make better information enter the network, thereby obtaining better accuracy and generalization. Finally, the gradient centralization is embedded in the stochastic gradient function, so as to improve the generalization performance of the network and make the training more efficient and stable. The experiment was performed on the OTB50 and VOT2018 data sets, and the improved algorithm achieved better performance than the original algorithm.

An improved object tracking algorithm based on adaptive weighted strategy and occlusion detection mechanism

Due to complex background and volatile object shape-appearance in image, the stability and accuracy of tracking algorithm is often disturbed and reduced. So how to accurately and robustly track object in object tracking application is a challenge topic at home and abroad. Built upon the methodologies of compressive tracking and spatio-temporal context, a simple yet robust object tracking method is proposed for solving the drift and occlusion problems in paper. It combines two existing classical ideas into a single framework: adaptive weighted idea and occlusion detection mechanism. In order to weaken interference problems of object background, object area is firstly partitioned into equal-sized sub-patches and the different weight related with location information is assigned for each patch; Then, for improving its robustness, Bhattacharyya distance is adopted to find out these samples with maximum discrimination; In addition, our proposed occlusion detection mechanism is for recapturing the tracked object when occlusion occurs. Many simulation experiments show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms in handing various challenging infrared videos, especially occlusion and shape deformation.

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

Improving Object Tracking by Added Noise and Channel Attention.

CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.

Improved target tracking algorithm based on kernelized correlation filter

We propose an improved object tracking algorithm based on kernelized correlation filter (KCF), which can overcome the drawback of traditional KCF algorithms in that they cannot effectively adapt to target-scale variations and target occlusion in tracking. First, the target-scale pyramid is built, whose histogram of oriented gradients feature extracted of every layer multiplied by the correlation filters; the maximum response of current scale of the filters is the best target scale. In addition, the improved algorithm is combined with the improved correlation filter framework, and the background information around the target is appropriately increased. When the target is occluded, the background information can be effectively used to track the target. The proposed algorithm is validated on the benchmark evaluation and compared with the traditional algorithms, such as KCF and circulation structure of tracking-by-detection with kernel. The results indicate that our tracking accuracy can reach 66.9% and the success rate can be 58.2. When the target is scale variation, the accuracy and success rate increase by 1.1% and 10.3%, respectively, compared with KCF. If the tracked target is occluded, a second improved algorithm is compared with the detection algorithm which only adds the scale detection, the tracking accuracy increases by 8%, and the success rate increases by 4.9%.

Robust object tracking via integration of particle filtering with deep detection

We propose a video object tracker (IDPF-RP) which is built upon the variable-rate color particle filtering with two innovations: (i) A deep region proposal network guided candidate BB selection scheme based on the dynamic prediction model of particle filtering is proposed to accurately generate the qualified object BBs. The introduced region proposal alignment scheme significantly improves the localization accuracy of tracking. (ii) A decision level fusion scheme that integrates the particle filter tracker and a deep detector resulting in an improved object tracking accuracy is formulated. This enables us to adaptively update the target model that improves robustness to appearance changes arising from high motion and occlusion. Performance evaluation reported on challenging VOT2018/2017/2016 and OTB-50 data sets demonstrates that IDPF-RP outperforms state-of-the-art trackers especially under size, appearance and illumination changes. Our tracker achieves comparable mean accuracy on VOT2018 while it respectively provides about 8%, 15%, and 30% higher success rates on VOT2016, VOT2017 and OTB-50 when IoU threshold is 0.5.

A Winner-Take-All Strategy for Improved Object Tracking.

Recently, numerous state-of-the-art learning schemes are proposed for object tracking. However, typically, most methods can only solve certain type of challenges but are less effective for the rest-no single tracker is perfect for all challenges. In this paper, a winner-take-all (WTA) strategy is exploited to select a winner tracker (considering both accuracy and efficiency) from a set of prevailing methods to tackle the current challenge, according to features extracted from the current environment and an efficiency factor. To achieve this, a structural regression model to characterize the trackers is trained on a public dataset. By incorporating the complementary abilities from multiple trackers, the diversity of the model is improved so that the WTA tracker can tackle various unpredictable difficulties. Since only one tracker is selected at any time, the average efficiency of the proposed model is also higher than that of complex trackers in the tracker set. The proposed WTA framework is tested on two benchmark datasets as well as several long sequences, and extensive experimental results illustrate that WTA can significantly improve both the performance and the efficiency.

Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm

In this paper, a new robust mean shift tracker is proposed by utilizing the joint color and texture histogram. The contribution of our work is to take local phase quantization (LPQ) operator advantage of texture features representation, and to combine it with a color histogram mean shift tracking algorithm. The LPQ technique can be applied to obtain the texture features which represent the object. In texture classification, The LPQ operator is much robust to blur than the well-known local binary pattern operator (LBP). Compared with traditional color histogram mean shift algorithm which considers only color statistical information of the object, the joint color-LPQ texture histogram is more robust and overcome some difficulties of the traditional color histogram mean shift algorithm. Comparative experimental results on numerous challenging image sequences show that the proposed algorithm obtains considerably better performance than several state-of-the-art methods, especially traditional mean shift tracker. The algorithm is evaluated by numerical parameters: the center location and the average overlap, it proved the tracking robustness in presence of similar target appearance and background, motion blurring.

CAM shift와 8방향 탐색 윈도우를 이용한 객체 추적

ABSTRACT This research aims to suggest methods to improve object tracking performance by combining CAM shift algorithm with 8-way search window, and reduce arithmetic operation by reducing the number of frame used for tracking. CAM shift has its adverse effect in tracking methods using signature color or having difficulty in tracking rapidly moving object. To resolve this, moving search window of CAM shift makes it possible to more accurately track high-speed moving object after finding object by conducting 8-way search by using information at a final successful timing point from a timing point missing tracking object. Moreover, hardware development led to increased unnecessary arithmetic operation by increasing the number of frame produced per second, which indicates efficiency can be enhanced by reducing the number of frame used in tracking to reduce unnecessary arithmetic operation. 키워드 : 추적, 탐색, 캠 쉬프트, 탐색 윈도우 Key word : Tracking, Search, CAM shift, Search WindowJournal of the Korea Institute of Information andCommunication Engineering