Robust Tracking Algorithm Research Articles

Robust underwater object tracking with image enhancement and two-step feature compression

Developing a robust algorithm for underwater object tracking (UOT) is crucial to support the sustainable development and utilization of marine resources. In addition to open-air tracking challenges, the visual object tracking (VOT) task presents further difficulties in underwater environments due to visual distortions, color cast issues, and low-visibility conditions. To address these challenges, this study introduces a novel underwater target tracking framework based on correlation filter (CF) with image enhancement and a two-step feature compression mechanism. Underwater image enhancement mitigates the impact of visual distortions and color cast issues on target appearance modeling, while the two-step feature compression strategy addresses low-visibility conditions by compressing redundant features and combining multiple compressed features based on the peak-to-sidelobe ratio (PSR) indicator for accurate target localization. The excellent performance of the proposed method is demonstrated through evaluation on two public UOT datasets.

Robust and Flexible Maritime ISAR Tracking Algorithm for Multiple Maneuvering Extended Vessels in Heavy-Tailed Clutter Using Skewed Multiple Model MB-Sub-RMM-TBD Filter

Robust and Flexible Maritime ISAR Tracking Algorithm for Multiple Maneuvering Extended Vessels in Heavy-Tailed Clutter Using Skewed Multiple Model MB-Sub-RMM-TBD Filter

Environmental effects on the experimental modal parameters of masonry buildings: experiences from the Italian Seismic Observatory of Structures (OSS) network

AbstractThe paper presents an in-depth analysis of the ambient dynamic behavior of nine masonry buildings monitored by the Italian Seismic Observatory of Structures (OSS). Addressing a significant knowledge gap affecting this structural type, the study reveals how daily and seasonal fluctuations in environmental factors have a notable influence on its experimental modal parameters. A robust frequency-domain tracking algorithm is first developed to identify and follow the evolution of modal parameters over time, exploiting ambient vibration recordings acquired at sub-daily intervals on the structures. The procedure is systematically applied to the entire portfolio of case-study buildings and, in the first year of training, integrated with measurements of environmental parameters provided by nearby weather stations. The multivariate regression analysis indicates that temperature variation is the primary driver of the observed wandering of natural frequencies. The frequency–temperature relationship shows a positive correlation above zero degrees and, in several cases, a significant degree of nonlinearity already present in low-frequency global modes. Simple predictive models are proposed to address such nonlinear behavior, including freezing conditions and accounting for internal heating during winter. Leveraging these novel insights, the work develops strategies to improve the efficiency of data acquisition protocols and training periods, enabling the near-future extension of real-time condition assessment methodologies to the entire OSS network.

Robust pedestrian multi-object tracking in the intelligent bus environment

Abstract Pedestrian multi-object tracking algorithms aim to maintain identity information of pedestrians by comparing the similarity between trajectories and detections, predicting pedestrian motion trajectories. However, within the context of intelligent bus, challenges arise due to factors such as passenger growth and vehicle vibrations, rendering existing pedestrian multi-object tracking algorithms less accurate. Therefore, this paper proposes an intelligent bus terminal robust pedestrian multi-object tracking algorithm named pure motion (PM), which can consistently and stably track pedestrians. The proposed algorithm employs several key strategies. Firstly, it optimizes trajectory prediction by adapting the aspect ratio of the prediction box based on pedestrian movement, automatically adjusting its shape, and selecting velocity weight coefficients according to different tracking targets. Secondly, it decomposes the homography matrix to acquire motion components and correct predicted results under motion conditions. Subsequently, the algorithm leverages the similarity between detection results and trajectories to retain high-confidence detections, eliminating low-confidence ones associated with background, thereby reducing false negatives and enhancing trajectory coherence. Futhermore, the introduction of detection confidence into trajectory updates to enhances the precision of measurement noise. The proposed algorithm underwent testing in intelligent bus driving scenarios, including turns, waiting for traffic lights, emergency braking, and approaching bus stops. The tracking accuracy on the MOT17-13-val dataset reaches 81.8. The results demonstrate that PM significantly improves the robustness of pedestrian multi-object tracking algorithms in the environment of intelligent bus.

A multi-target detection and position tracking algorithm based on mmWave-FMCW radar data

Detecting and tracking the position of multiple targets indoors is a challenging measurement problem due to the inherent difficulty to cluster correctly the sensor data associated to a given target and to track the position of each cluster with adequate accuracy. This problem is critical especially in rooms filled with fixed or moving objects hampering target detection and whenever the paths of different targets cross one another. In this paper, a robust Multiple Targets Tracking (MTT) algorithm exploiting the clouds of points collected from a mmWave-FMCW radar is presented. The proposed solution consists of four main steps. First, the possible outliers of a raw radar data set are removed using a neural network model. Next, the cleaned-up radar data are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then, a Kalman Filter (KF) is used to track the position of the centroid of each cluster. Finally, a Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm is applied and updated over reasonably short time intervals to decide which detected tracks are supposed to be confirmed and which ones instead should be discarded. The proposed MTT technique was validated experimentally using the data sets collected from a 60-GHz TI IWR6843 radar platform. The reported results show that the developed algorithm, if properly tuned, is faster and returns more accurate results than other MTT techniques. In particular, the percentage of detection errors is negligible and the planar positioning accuracy is within about 30 cm with 90% probability when up to five targets move freely within the same room.

A simplified control algorithm for efficient and robust tracking of the maximum power point in PV systems

This article presents a novel two-stage simplified control algorithm designed to enhance the Maximum Power Point Tracking (MPPT) operation of PV systems while minimizing the complexity associated with it. A voltage-based hybrid beta-incremental conductance (hybrid-beta-INC) is developed in the first stage to determine the MPP voltage. The methodology behind this scheme is to first approach the neighborhood of the MPP through an intermediate variable β, which has a simple monotonically decreasing relationship with the PV voltage. Subsequently, the INC is employed to target the actual MPP voltage. The steady-state response of the PV system is thus maintained throughout this stage by the hybrid-beta-INC. In the second stage, a simplified parameter-less (SP) controller is proposed to regulate the underdamped dynamics of the PV. The system's stability as a whole is enhanced through the decoupling of these two phases. In contrast to currently available MPPT controllers, the proposed SP makes a significant contribution to the structural simplicity of the control framework by eliminating the need for adjustable parameters. Simplicity, enhanced control immunity and resilience to load disturbances and parametric uncertainties are the main attributes of the proposed controller. Its superior attributes over exiting controllers such as the P&O, INC, search space, and hybrid INC-integral backstepping nonlinear controllers, is affirmed on the basis of quantitative metrics such as the tracking time, ripples and efficiency. Finally, the performance of the proposed system is monitored under real-time environmental settings for three consecutive days, which confirms its resilience and dependability in tracking the MPP of the PV system under experimental conditions. The results showed that the controller is able to cope with climatic and load changes maintaining a robust response. It is unveiled that it maintains a minimum efficiency of 99.72% despite fluctuations in temperature. In the presence of continually changing irradiance conditions, the EN 50,530 test demonstrates an efficacy in excess of 99.95%.

A Novel Sea Target Tracking Algorithm for Multiple Unmanned Aerial Vehicles Considering Attitude Error in Low-Precision Geodetic Coordinate Environments

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments.

Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen.

Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.

A Comparative Study of Principled rPPG-Based Pulse Rate Tracking Algorithms for Fitness Activities.

Performance improvements obtained by recent principled approaches for pulse rate(PR) estimation have typically been achieved by adding or modifying certain modules within a reconfigurable system. Yet, evaluations are usually performed only at the system level. To better understand each module's contribution and facilitate future research in explainable learning and artificial intelligence for physiological monitoring, this paper conducts a comparative study of video-based, principled PR tracking algorithms, with a particular focus on challenging fitness scenarios. A review of the progress achieved over the last decade and a half in this field is utilized to construct the major processing modules of a reconfigurable remote pulse rate sensing system. Experiments are conducted on two challenging datasets-an internal collection of 25 videos of two Asian males exercising on stationary-bike, elliptical, and treadmill machines and 34 videos from a public ECG fitness database of 14 men and 3 women exercising on elliptical and stationary-bike machines. The signal-to-noise ratio (SNR), Pearson's correlation coefficient, error count ratio, error rate, and root mean squared error are used for performance evaluation. The top-performing configuration produces respective values of -0.8dB, 0.86, 9%, 1.7%, and 3.3beats per minute(bpm) for the internal dataset and 1.3dB, 0.77, 28.6%, 6.0%, and 8.1bpm for the ECG Fitness dataset, achieving significant improvements over alternative configurations. Our results indicate a synergistic effect between pulse color mapping and adaptive motion filtering, as well as the importance of a robust frequency tracking algorithm for PR estimation in low SNR settings.

Cow detection and tracking system utilizing multi-feature tracking algorithm

In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance.

Recursive identification of the time-varying frequency parameters of a space solar power satellite using a robust subspace tracking method based on the variable forgetting factor

Considering changes in the structural configuration caused by the rotation of solar panel arrays in space solar power satellites, this study proposes an improved robust recursive subspace tracking algorithm to identify the time-varying modal frequency parameters of the system. In this algorithm, variable forgetting factor and robust weight coefficient are involved to improve the tracking adaptability of time-varying system and decrease the influence of impulsive disturbance. Then, the time-varying pseudo modal frequency parameters are identified using the recursive projection subspace tracking. In the numerical simulations, the influence of the different rotation speeds of the solar panel arrays on the frequency identification results is studied. The computational results for the different rotation speeds reveal that the tracking performance of the proposed algorithm is better than that of the original method for time-varying system. Under the influence of impulsive disturbances, the relative values of the average relative error under the proposed algorithm are approximately 65.9–75.2% less than those under the original algorithm. Additionally, the results reveal that when the rotation speed of the solar panel arrays is time-varying, the proposed algorithm achieves a better tracking performance than the conventional recursive methods by the adjustment of the variable forgetting factor. When the signal-to-noise ratio is 15 dB, the absolute and relative values of the average relative error under the proposed algorithm are less by approximately 6.4% and 64.8%, respectively, under the two reference methods with a fixed forgetting factor.

Robust online learning based on siamese network for ship tracking

The complex and changeable inland river scenes resulting out of frequent occlusions of ships in the available tracking methods are not accurate enough to estimate the motion state of the target ship leading to object tracking drift or even loss. In view of this, an attempt is made to propose a robust online learning ship tracking algorithm based on the Siamese network and the region proposal network. Firstly, the algorithm combines the off-line Siamese network classification score and the online classifier score for discriminative learning, and establishes an occlusion determination mechanism according to the classification the fusion score. When the target is in the occlusion state, the target template is not updated, and the global search mechanism is activated to relocate the target, thereby avoiding object tracking drift. Secondly, an efficient adaptive online update strategy, UpdateNet, is introduced to improve the template degradation in the tracking process. Finally, on comparing the state-of-the-art tracking algorithms on the inland river ship datasets, the experimental results of the proposed algorithm show strong robustness in occlusion scenarios with an accuracy and success rate of 56.8% and 57.2% respectively. Supportive source codes for this research are publicly available at https://github.com/Libra-jing/SiamOL.

A robust signal tracking algorithm for GPS and NavIC constellations

A robust signal tracking algorithm for GPS and NavIC constellations

Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking.

The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient-derived) pluripotent stem cell (hPSC)-derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub-pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state-of-the-art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function.

Detection, Localization, and Tracking of Multiple MAVs With Panoramic Stereo Camera Networks

Malicious use of micro aerial vehicles (MAVs) has become a serious threat to public safety and personal privacy in recent years. Motivated by this problem, we propose a systematic approach to monitor the intrusion of malicious MAVs based on a novel type of panoramic stereo camera networks. Each sensing node of such a network consists of 16 lenses that can form a 360-degree panoramic vision system. The 16 lenses further form 8 pairs of stereo cameras that can directly localize aerial targets. The effective range for a sensing node localizing a MAV like DJI M300 could reach 80 meters, which is much farther than existing commercial stereo cameras. In terms of algorithms, we propose i) a novel visual MAV detection algorithm based primarily on motion features of MAVs, ii) an efficient stereo localization algorithm based on sparse feature points, and iii) robust multi-target tracking and trajectory fusion algorithm to fuse the observations of different sensing nodes. The effectiveness, robustness, and accuracy of the proposed algorithms together with the overall system have been verified by extensive experimental tests. To the best of our knowledge, this is the first systematic approach to detect, localize, and track unknown MAVs in the literature. Our approach provides a scalable solution to securely cover large areas of interest against malicious MAV intrusion. Note to Practitioners—Micro aerial vehicles (MAVs) have been widely used in many domains nowadays. However, they have also brought many safety problems. To monitor the intrusion of malicious MAVs, this paper proposes a novel type of panoramic stereo camera networks that can detect, localize, and track multiple MAVs simultaneously. Such a network consists of a number of sensing nodes and a central node. Each sensing node is able to detect, localize, and track multiple MAV targets. The role of the central node is to fuse the observations from multiple sensing nodes to generate more accurate trajectories of the MAV targets and in the meantime secure a large area in a coordinated way. This paper presents the details of the prototype of the system and the key algorithms therein.

Innovative sensorless dual-axis solar tracking system using particle filter

Renewable energy (RE), particularly solar photovoltaic (PV) systems, is a promising alternative to traditional energy sources for worldwide electricity generation. In recent years, PV generation capacity has increased rapidly due to a considerable reduction in installation costs. Nevertheless, in PV systems, energy generation is significantly less than the installed capacity due to the PV modules’ orientation. Hence, this study proposes an innovative solar tracking system to enhance PV energy generation. Previous research classified solar tracking systems into two categories: sensor-based and sensorless solar tracking systems. However, tracking performance inevitably deteriorates when implementing sensor-based solar tracking systems in low-visibility conditions. Conversely, sensorless solar tracking systems require substantial historical meteorological data based on geographic location and complex mathematical models estimating the sun’s position, increasing computational complexity. This study aims to overcome these difficulties by proposing a sensorless dual-axis solar tracking system that does not require historical meteorological data, complex mathematical models, and sun position sensors to track the sun’s position. Particle filter (PF), a robust sampling-based tracking algorithm, is applied to develop an innovative solar tracking strategy. Initially, a set of samples (also called particles) is generated as inputs to PF corresponding to the proposed tracking system’s orientation angles: daily and elevation angles. Next, PV power is captured as a measurement of each particle, and a weight associated with the PV power is estimated to represent each particle’s relative significance. Furthermore, each particle is estimated and updated recursively, considering its measurement to determine the sun’s probable position. The tracking strategy is terminated once the particles’ weights are equal, indicating that the optimal position has been achieved (convergence). A comparative study is conducted over 60 days under various weather conditions to evaluate the proposed tracking system’s performance compared with that of a fixed flat-plate system. The experimental results indicate that the proposed tracking system improves energy generation performance (after accounting for operational energy consumption) by 20.1% compared with the fixed flat-plate system.

Hand Gesture Recognition System for Deaf and Dumb People

Deaf and dumb people communicate using language known as Sign language. There are various sign languages but comparing to other sign languages, Indian Sign Language interpretation has got least mindfulness by researchers. Alphabets in English sign language and various phrases can be recognized using the application. It deals with images of bare hands. Hand-Gesture recognition is the ability to recognize hand-gesture. The hand gesture recognition system is solution to this problem which uses the image of hand gesture and recognize the sign present in the image. These features based on gesture analysis of the hand on which image of sign is shown and it extract slant and slope information. System provides an opportunity for deaf and dumb people to communicate with normal people effortlessly. Our research focuses on recognition of English sign language for vast reach of audience. There have been several upgrades in technology and a lot of research has been done to aid the deaf and dumb people. Aiding the cause, Deep learning along with computer vision is being used to make an impact on this cause. It can help the deaf and dumb people in communicating with others as many of us don’t know what sign language is. In this sign language recognition project, we create an kotlin application, which detects numbers from 1 to 10 and also the English alphabets. The initial step of this system is to build a database of English Sign Language. Hand gesture detection is the most determining step in each hand gesture recognition system since if we get better segmented output, better recognition rates can be achieved. The proposed system also includes efficient and robust hand segmentation and tracking algorithm to achieve finer recognition rates. Various isolated words from the Standard English sign language have been recognized using a large set of samples. In proposed system, we intend to recognize some very basic elements of sign language and to translate them to text .

A markerless beam’s eye view tumor tracking algorithm based on unsupervised deformable registration learning framework of VoxelMorph in medical image with partial data

PurposeTo propose an unsupervised deformable registration learning framework-based markerless beam's eye view (BEV) tumor tracking algorithm for the inferior quality megavolt (MV) images with occlusion and deformation. MethodsQuality assurance (QA) plans for thorax phantom were delivered to the linear accelerator with artificially treatment offsets. Electronic portal imaging device (EPID) images (682 in total) and corresponding digitally reconstructed radiograph (DRR) were gathered as the moving and fixed image pairs, which were randomly divided into training and testing set in a ratio of 0.7:0.3 to train a non-rigid registration model with Voxelmorph. Simultaneously, 533 pairs of patient images from 21 lung tumor plans were acquired for tumor tracking investigation to offer quantifiable tumor motion data. Tracking error and image similarity measures were employed to evaluate the algorithm’s accuracy. ResultsThe tracking algorithm can handle image registration with non-rigid deformation and losses ranging from 10 % to 80 %. The tracking errors in the phantom study were below 3 mm in about 86.8 % of cases, and below 2 mm in about 80 % of cases. The normalized mutual information (NMI) changes from 1.182 ± 0.024 to 1.198 ± 0.024 (p < 0.005). The patient target had an average translation of 3.784 mm and a maximum comprehensive displacement value of 7.455 mm. NMI of patient images changes from 1.209 ± 0.027 to 1.217 ± 0.026 (p < 0.005), indicating the presence of non-negligible non-rigid deformation. ConclusionsThe study provides a robust markerless tumor tracking algorithm for multi-modal, partial data and inferior quality image processing.

Multi-feature-Based Robust Cell Tracking.

Cell tracking algorithms have been used to extract cell counts and motility information from time-lapse images of migrating cells. However, these algorithms often fail when the collected images have cells with spatially and temporally varying features, such as morphology, position, and signal-to-noise ratio. Consequently, state-of-the-art algorithms are not robust or reliable because they require manual inputs to overcome the cell feature changes. To address these issues, we present a fully automated, adaptive, and robust feature-based cell tracking algorithm for the accurate detection and tracking of cells in time-lapse images. Our algorithm tackles measurement limitations twofold. First, we use Hessian filtering and adaptive thresholding to detect the cells in images, overcoming spatial feature variations among the existing cells without manually changing the input thresholds. Second, cell feature parameters are measured, including position, diameter, mean intensity, area, and orientation, and these parameters are simultaneously used to accurately track the cells between subsequent frames, even under poor temporal resolution. Our technique achieved a minimum of 92% detection and tracking accuracy, compared to 16% from Mosaic and Trackmate. Our improved method allows for extended tracking and characterization of heterogeneous cell behavior that are of particular interest for intravital imaging users.