Robust Tracking Research Articles

An Optimized Position Control via Reinforcement-Learning-Based Hybrid Structure Strategy

Most control system implementations rely on single structures optimized for specific performance criteria through rigorous derivation. While effective for their intended purpose, such controllers often underperform in areas outside their primary optimization focus and involve performance trade-offs. A notable example is the Internal Model Principle (IMP) controller, renowned for its robustness and precision in reference tracking under periodic disturbances. However, IMP controllers exhibit poor transient-state performance, characterized by significant overshoot and oscillatory responses, which remains a persistent challenge. To address this limitation, this paper proposes a reinforcement learning (RL)-based hybrid control scheme that overcomes the trade-off in IMP controllers between achieving zero steady-state tracking error and a fast transient response. The proposed method integrates a cascade control structure, optimized for transient-state performance, with an IMP controller, optimized for robust reference tracking under sinusoidal disturbances, through switching logic governed by a Deep Q-Network model. Smooth transitions between control modes are ensured using an internal state update mechanism. The proposed approach is validated through simulations and experimental tests on a direct current (DC) motor position control system. The results demonstrate that the hybrid structure effectively resolves the trade-off associated with IMP controllers, yielding improved performance metrics, such as rapid convergence to the reference, reduced transient overshoot, and enhanced nominal performance recovery against disturbances.

Abstract 4089: A novel NGS-based sample tracking system for robust sample chain of custody and quality control in fresh frozen and FFPE tumor samples

Abstract When processing large cohorts of samples, sample identity and functional quality are key to ensure the derived data is accurate and attributed to the appropriate sample. This is especially critical when working with precious and degraded material such as formalin-fixed paraffin-embedded (FFPE) samples. A potential sample swap or poor sample quality may result in incorrect data or delays due to re-processing. While traditional methods such as qPCR can be used for quality control, they may not be an effective predictor of sample success in Next-Generation Sequencing (NGS) based assays due to differential susceptibility to inhibitors and interfering substances. Performing a functional quality control assessment using the same methodology for data generation allows for a more accurate prediction of performance as it would be similarly affected by interfering substances or sample degradation. We have created a scalable, cost-effective sample tracking and functional quality control assay that may be assessed prior to performing more expensive downstream sequencing assays. This assay was designed to confirm consistency in sample identity during processing and confirm identity when multiple samples are submitted from the same subject. This amplicon panel targets autosomal SNPs with varying minor allele frequencies in different ethnic populations, limiting a bias towards variation in European ethnic populations. The average minor allele frequency for the panel developed for the general population is 0.45, more specifically 0.46 for European background, 0.44 for African background and 0.46 for Asian background based on data from the Allele Frequency Aggregator (ALFA) database. Most amplicons were designed to be less than 150 base pairs to increase the likelihood of success for samples with degraded DNA. Targets were included on the X and Y chromosome for sex determination. Tumor-normal paired samples were processed on the sample tracking assay, targeting regions throughout the exome to allow tracking of sample identity. When comparing “normal” samples extracted from blood to their paired FFPE tumor tissue, known matches had an average of 98.0% concordance, while known mismatches had an average of 40.4% concordance, with the highest known mismatch showing 61.9% concordance. Additionally, when comparing well-characterized samples to their corresponding data from the 1000 Genomes Project, samples had 99.4% concordance. This exemplifies a robust sample tracking assay that can ensure sample identity is correct. A strong quality control assay enables NGS laboratories to identify poor sample quality or degradation, allowing for re-extraction or new sample collection before processing on a more expensive assay. This newly developed sample tracking assay ensures both sample integrity and identity, leading to an overall increase in data quality for cancer research studies. Citation Format: Katherine Milligan, Yu Qiu, Andrea O'Hara, Carrie Ziemniak, David Corney, Haythem Latif. A novel NGS-based sample tracking system for robust sample chain of custody and quality control in fresh frozen and FFPE tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4089.

Adaptive Terminal Sliding Mode Control for a Quadrotor System with Barrier Function Switching Law

This study presents a novel finite-time robust control framework for quadrotor systems subjected to model uncertainties and unknown external disturbances. A fast terminal sliding mode (FTSM) manifold is first constructed to achieve finite-time convergence of tracking errors. To address the challenges posed by uncertain system dynamics, a radial basis function neural network (RBFNN) is integrated for real-time approximation of unknown nonlinearities. In addition, an adaptive gain regulation mechanism based on a barrier Lyapunov function (BLF) is developed to ensure boundedness of system trajectories while enhancing robustness without requiring prior knowledge of disturbance bounds. The proposed control scheme guarantees finite-time stability, strong robustness, and precise trajectory tracking. Numerical simulations substantiate the efficacy and superiority of the proposed method in comparison with existing control approaches.

Robust Optimal Path Tracking Control for Intelligent Vehicles Equipped with Wheel Modules Based on Continuous Integral Sliding Mode Control

Robust Optimal Path Tracking Control for Intelligent Vehicles Equipped with Wheel Modules Based on Continuous Integral Sliding Mode Control

Robust Tracking via Mamba-based Context-aware Token Learning

How to make a good trade-off between performance and computational cost is crucial for a tracker. However, current famous methods typically focus on complicated and time-consuming learning that combining temporal and appearance information by input more and more images (or features). Consequently, these methods not only increase the model's computational source and learning burden but also introduce much useless and potentially interfering information. To alleviate the above issues, we propose a simple yet robust tracker that separates temporal information learning from appearance modeling and extracts temporal relations from a set of representative tokens rather than several images (or features). Specifically, we introduce one track token for each frame to collect the target's appearance information in the backbone. Then, we design a mamba-based Temporal Module for track tokens to be aware of context by interacting with other track tokens within a sliding window. This module consists of a mamba layer with autoregressive characteristic and a cross-attention layer with strong global perception ability, ensuring sufficient interaction for track tokens to perceive the appearance changes and movement trends of the target. Finally, track tokens serve as a guidance to adjust the appearance feature for the final prediction in the head. Experiments show our method is effective and achieves competitive performance on multiple benchmarks at a real-time speed.

Foundation Model Driven Appearance Extraction for Robust Multiple Object Tracking

Multiple Object Tracking (MOT) is a fundamental task in computer vision. Existing methods utilize motion information or appearance information to perform object tracking. However, these algorithms still struggle with special circumstances, such as occlusion and blurring in complex scenes. Inspired by the fact that people can pinpoint objects through verbal descriptions, we explore performing long-term robust tracking using semantic features of objects. Motivated by the success of the multimodal foundation model in text-image alignment, we reconsider the appearance feature extraction module in MOT and propose a Foundation model Driven multi-object tracker (FDTracker). Specifically, we propose a two-stage trained appearance feature extractor. In the first stage, using a single image of the object as input, the model could capture the attributes of objects with the assistance of natural language instructions. In the second stage, using a sequence of images of objects as input, the model learns how to use these attributes to distinguish between different objects and connect the same object at different times. Finally, for coordinating appearance and motion information, we propose a reasonable combined strategy, which better facilitates trajectory assignment and reconnection. Extensive experiments on benchmarks demonstrate the robustness of FDTracker.

RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba

Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust multimodal representation, due to large computational burden. To address this issue, this paper presents a novel All-layer multimodal Interaction Network, named AINet, which performs efficient and effective feature interactions of all modalities and layers in a progressive fusion Mamba, for robust RGBT tracking. Even though modality features in different layers are known to contain different cues, it is always challenging to build multimodal interactions in each layer due to struggling in balancing interaction capabilities and efficiency. Meanwhile, considering that the feature discrepancy between RGB and thermal modalities reflects their complementary information to some extent, we design a Difference-based Fusion Mamba (DFM) to achieve enhanced fusion of different modalities with linear complexity. When interacting with features from all layers, a huge number of token sequences (3840 tokens in this work) are involved and the computational burden is thus large. To handle this problem, we design an Order-dynamic Fusion Mamba (OFM) to execute efficient and effective feature interactions of all layers by dynamically adjusting the scan order of different layers in Mamba. Extensive experiments on four public RGBT tracking datasets show that AINet achieves leading performance against existing state-of-the-art methods. We will release the code upon acceptance of the paper.

Exploring Enhanced Contextual Information for Video-Level Object Tracking

Contextual information at the video level has become increasingly crucial for visual object tracking. However, existing methods typically use only a few tokens to convey this information, which can lead to information loss and limit their ability to fully capture the context. To address this issue, we propose a new video-level visual object tracking framework called MCITrack. It leverages Mamba's hidden states to continuously record and transmit extensive contextual information throughout the video stream, resulting in more robust object tracking. The core component of MCITrack is the Contextual Information Fusion module, which consists of the mamba layer and the cross-attention layer. The mamba layer stores historical contextual information, while the cross-attention layer integrates this information into the current visual features of each backbone block. This module enhances the model's ability to capture and utilize contextual information at multiple levels through deep integration with the backbone. Experiments demonstrate that MCITrack achieves competitive performance across numerous benchmarks. For instance, it gets 76.6% AUC on LaSOT and 80.0% AO on GOT-10k, establishing a new state-of-the-art performance.

MambaLCT: Boosting Tracking via Long-term Context State Space Model

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through a selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model's ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time runing speeds.

Influence of cracks on the nonlinear dynamic behaviors of pipes conveying fluid

This study investigates the stability and nonlinear dynamic characteristics of surface-cracked pipes under the interaction of internal fluids. Based on the Euler-Bernoulli beam theory, the equations of motion for simply supported pipes conveying fluid are derived using Hamilton’s principle. With the assumption that the crack opening remains constant, the local flexibility theory is employed to establish an ‘elastic hinge’ to represent the cracks. In the numerical simulations, the effects of crack location, crack angle, internal fluid velocity, and external excitation on the dynamics of the system are considered. The stability of the pipe is investigated by observing the variations of the natural frequencies with these parameters. The Arc-length continuation method is used to calculate the amplitude-frequency response of the cracked pipe. The influence of the crack property and the flow velocity on the nonlinear primary resonance of the pipe are discussed. Compared with the Runge-Kutta method, this method has stronger robustness and adaptability in the solution path tracking of parametric nonlinear systems. From the simulation, we observed that an increase in fluid velocity leads to enhanced system instability. The presence of cracks reduces the system’s stiffness and lowers its natural frequency. Additionally, the location, depth, and angle of the cracks exhibit a positive correlation with the system’s instability. These research results contribute to advancing safety management measures for pipeline systems and reducing potential accident losses.

Robust UAV Target Tracking Algorithm Based on Saliency Detection

Due to their high efficiency and real-time performance, discriminant correlation filtering (DCF) trackers have been widely applied in unmanned aerial vehicle (UAV) tracking. However, the robustness of existing trackers is still poor when facing complex scenes, such as background clutter, occlusion, camera motion, and scale variations. In response to this problem, this paper proposes a robust UAV target tracking algorithm based on saliency detection (SDBCF). Using saliency detection methods, the DCF tracker is optimized in three aspects to enhance the robustness of the tracker in complex scenes: feature fusion, filter-model construct, and scale-estimation methods improve. Firstly, this article analyzes the features from both spatial and temporal dimensions, evaluates the representational and discriminative abilities of different features, and achieves adaptive feature fusion. Secondly, this paper constructs a dynamic spatial regularization term using a mask that fits the target, and integrates it with a second-order differential regularization term into the DCF framework to construct a novel filter model, which is solved using the ADMM method. Next, this article uses saliency detection to supervise the aspect ratio of the target, and trains a scale filter in the continuous domain to improve the tracker’s adaptability to scale variations. Finally, comparative experiments were conducted with various DCF trackers on three UAV datasets: UAV123, UAV20L, and DTB70. The DP and AUC scores of SDBCF on the three datasets were (71.5%, 58.9%), (63.0%, 57.8%), and (72.1%, 48.4%), respectively. The experimental results indicate that SDBCF achieves a superior performance.

Dynamic sliding mode based trajectory tracking control of fully and under-actuated quadrotor with perturbations

In this work, we present a robust dynamic sliding mode controller (DSMC) for the position tracking and attitude stabilization of a quadrotor. The proposed controller overcomes the problem of chattering in classical sliding mode control while preserving the advantages of conventional sliding surfaces. The mathematical model of the quadrotor is decomposed into a fully actuated, and an under-actuated subsystem, and DSMC is designed for each of them. The proposed controller is observed to give a high precision robust tracking performance for up to 80% inertial uncertainties present in the plant model. The stability of the overall non-linear system is proved using Lyapunov theory. Finally, simulation results are presented to test the efficacy of the control allocation in the presence of external disturbances.

A novel Half-To-All MOTR approach for robust video text tracking with incomplete annotations

A novel Half-To-All MOTR approach for robust video text tracking with incomplete annotations

Optimized Adaptive Fuzzy Synergetic Controller for Suspended Cable-Driven Parallel Robots

A suspended cable-driven parallel robot is a type of lightweight large-span parallel robot. The stability and control of this multi-input multi-output robot are studied in this work to overcome its inherited vulnerability to disturbance. An adaptive fuzzy synergetic controller is proposed to overcome these issues, combining synergetic control theory with adaptive fuzzy logic to ensure robust trajectory tracking. The parameters of the controller are optimized using the Dragonfly Algorithm, a metaheuristic technique known for its simplicity and fast convergence. The adaptive fuzzy synergetic controller is tested on a suspended cable-driven parallel robot model under both disturbance-free and disturbed conditions, demonstrating global asymptotic stability and superior tracking accuracy compared to existing controllers. Simulation results show the proposed controller achieves minimal tracking error and improved robustness in the presence of dynamic uncertainties, validating its practical applicability in industrial scenarios. The findings highlight the effectiveness of integrating synergetic control, fuzzy logic adaptation, and optimization for enhancing the performance and reliability of suspended cable-driven parallel robots.

Inventrove with Real-Time Face Recognition Using AI

Efficient workforce management is crucial in in- dustrial environments where precise monitoring of employee attendance directly impacts productivity and operational ef- ficiency. This paper presents the design and implementation of an advanced attendance management system tailored for industrial applications, which integrates biometric authentication and facial recognition technologies. The system leverages real- time data processing capabilities to ensure accurate and auto- mated tracking of employee attendance, eliminating traditional manual errors and enhancing security. The proposed solution utilizes deep learning algorithms for facial recognition, coupled with biometric verification, to provide a robust, scalable, and efficient attendance tracking mechanism. The system also ensures seamless data integration with enterprise resource planning (ERP) solutions, thus enhancing decision-making and workforce analytics. This study explores the architectural framework, im- plementation methodologies, and performance evaluation of the system in an industrial setting. The experimental results indicate a significant improvement in accuracy and efficiency compared to conventional attendance management approaches. Index Terms—Attendance management, facial recognition, biometric authentication, real-time data processing, industrial workforce tracking, deep learning, ERP integration.

Generalization to Type 2 of PSO-Optimized Type 1 PD Fuzzy Controller and its Application to a Quadrotor UAV

This study presents a new Type 2 fuzzy logic (interval-valued fuzzy logic) control system for a Vertical Take Off and Landing (VTOL) quadrotor. The goal of the control design is to obtain robust and stable tracking of the desired angles in the presence of disturbances and noises. The membership functions relative to the linguistic variables of the fuzzy if-then rules are chosen to control the quadrotor to track a reference trajectory. The Particle Swarm Optimization (PSO) method is used to optimize controller-free parameters. The results obtained from an extensive simulation study show that the quadrotor can operate autonomously in flight with stable orientation. The performance of the controller was evaluated for both Type 1 and Type 2 fuzzy controllers. The simulation results show the effectiveness of the proposed controller and its better performance.

A novel robust tracking algorithm for anti-UAV based on dynamic similarity scale estimation and adaptive occlusion-aware

A novel robust tracking algorithm for anti-UAV based on dynamic similarity scale estimation and adaptive occlusion-aware

Dynamic Event-Triggered Robust Feedback Model Predictive Tracking Control of Air-Breathing Hypersonic Vehicle Based on Disturbance Preview

Dynamic Event-Triggered Robust Feedback Model Predictive Tracking Control of Air-Breathing Hypersonic Vehicle Based on Disturbance Preview

Robust visual tracking using temporal regularization correlation filter with high-confidence strategy

Robust visual tracking using temporal regularization correlation filter with high-confidence strategy

Robust event-triggered tracking control for an unmanned aerial vehicle with non-vanishing disturbances

Robust event-triggered tracking control for an unmanned aerial vehicle with non-vanishing disturbances