Continuous Tracking Research Articles

In vivo profiling of phytocannabinoids in Cannabis spp. varieties via SPME-LC-MS analysis

BackgroundIn vivo solid-phase microextraction (SPME) is a minimally invasive, non-exhaustive sample-preparation technique that facilitates the direct isolation of low molecular weight compounds from biological matrices in living systems. This technique is especially useful for the analysis of phytocannabinoids (PCs) in plant material, both for forensic purposes and for monitoring the PC content in growing Cannabis spp. plants. In contrast to traditional extraction techniques, in vivo SPME enables continuous tracking of the changes in the level of PCs during plant growth without the need for plant material collection. In this study, in vivo SPME utilizing biocompatible C18 probes and liquid-chromatography coupled to quadrupole time-of flight mass spectrometry (LC-Q-TOF-MS) is proposed as a novel strategy for the extraction and analysis of the acidic forms of five PCs in growing medicinal cannabis plants. ResultsThe SPME method was optimized by testing various parameters, including the extraction phase (coating), extraction and desorption times, and the extraction temperature. The proposed method was validated with satisfactory analytical performance regarding linearity (10–3000 ng/mL), limits of quantification, and precision (relative standard deviations below 5.5 %). The proposed method was then successfully applied for the isolation of five acidic forms of PCs, which are main components of growing medicinal cannabis plants. As a proof-of-concept, SPME probes were statically inserted into the inflorescences of two varieties of Cannabis spp. plants (i.e., CBD-dominant and Δ9-THC-dominant) cultivated under controlled conditions for 30 min extraction of tetrahydrocannabinolic acid (Δ9-THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabiviarinic acid (CBVA), and tetrahydrocannabivarinic acid (THCVA). Significance and noveltyThe results confirmed that the developed SPME-LC-Q-TOF-MS method is a precise and efficient tool that enables direct and rapid isolation and analysis of PCs under in vivo conditions. The proposed methodology is highly appealing option for monitoring the metabolic pathways and compositions of multiple PCs in medicinal cannabis at different stages of plant growth.

Absolute Photoelectric Encoder Based on Position-Sensitive Detector Sensor

In response to the engineering, miniaturization, and high measurement accuracy requirements of encoders, this paper proposes a new type of absolute photoelectric encoder based on a position-sensitive detector (PSD). It breaks the traditional encoder’s code track design and adopts a continuous and transparent code track design, which has the advantages of small volume, high angle measurement accuracy, and easy engineering. The research content of this article mainly includes the design of a new code disk, decoding circuit, linear light source, and calibration method. The experimental results show that the encoder designed in this article has achieved miniaturization, simple installation and adjustment, and easy engineering. The volume of the encoder is Φ50 mm × 30 mm; after calibration, the resolution is better than 18 bits, and the accuracy reaches 5.4″, which further demonstrates the feasibility of the encoder’s encoding and decoding scheme.

Suppressing Ion Migration by Synergistic Engineering of Anion and Cation toward High-Performance Inverted Perovskite Solar Cells and Modules.

Ion migration-induced intrinsic instability and large-area fabrication pose a tough challenge for the commercial deployment of perovskite photovoltaics. Herein, an interface heterojunction and metal electrode stabilization strategy is developed by suppressing ion migration via managing lead-based imperfections. After screening a series of cations and nonhalide anions, the ideal organic salt molecule dimethylammonium trifluoroacetate (DMATFA) consisting of dimethylammonium (DMA+) cation and trifluoroacetate (TFA-) anion is selected to manipulate the surface of perovskite films. DMA+ enables the conversion of active excess and/or unreacted PbI2 into stable new phase DMAPbI3, inhibiting photodecomposition of PbI2 and ion migration. Meanwhile, TFA- can suppress iodide ion migration through passivating undercoordinated Pb2+ and/or iodide vacancies. DMA+ and TFA- synergistically stabilize the heterojunction interface and silver electrode. The DMATFA-treated inverted perovskite solar cells and modules achieve a maximum efficiency of 25.03% (certified 24.65%, 0.1 cm2) and 20.58% (63.74 cm2), respectively, which is the record efficiency ever reported for the devices based on vacuum flash evaporation technology. The DMATFA modification results in outstanding operational stability, as evidenced by maintaining 91% of its original efficiency after 1520 h of maximum power point continuous tracking.

Formation Tracking of Mobile Robots Under Obstacles Using Only an Active RGB-D Camera

This paper attempts to achieve leader-follower formation tracking for mobile robots under obstacles with only one RGB-D camera on the follower. In contrast, most existing research employs at least two sensors, including a camera and a LiDAR. However, the limited field-of-view (FOV) of RGB-D cameras introduces three significant challenges: intermittent observation of the leader, partial detection of obstacles, and conflicting observations of the leader and obstacles. To overcome these difficulties, we first propose a controller that enables the follower to achieve continuous observation and formation tracking of the leader without obstacles and depth images. To address the partial detection of obstacles, we design a rotating device that allows the camera to actively extend its observation range and resolve conflicts with leader detection. Based on camera feedback and the proposed controller, a multi-objective controller is presented that enables the follower to achieve formation tracking and obstacle avoidance simultaneously, without requiring communication or leader velocity. Simulations and experiments are conducted to demonstrate the feasibility of our methods.

The Zen Garden Virtual Reality App for eating disorders: description and preliminary results

IntroductionVirtual Reality (VR) represents an emerging and promising tool to enhance standard care for patients with eating disorders (EDs). Indeed, VR provides an immersive and interactive experience in a safe and controlled environment that can simulate real-life situations, showing encouraging findings on various components of psychological treatments such as exposure therapy, psychoeducation, and emotional regulation.ObjectivesThis study aims to evaluate the Zen Garden VR App in patients with anorexia nervosa (AN) in order to obtain pilot data regarding changes in mood, relaxation, anger, anxiety, and weight and shape concerns. A secondary aim was to receive feedback from participants about the VR experience, its components, and its possible application for people with AN.MethodsSelf-reported baseline and post-intervention data were collected from a sample of six female inpatients with AN recruited at the Eating Disorders Service at the Bethlem Royal Hospital of the South London and Maudsley NHS Foundation trust (SLaM). The technology used during the VR session consisted of an Oculus head-mounted display headset and two controllers which provided continuous rotational and positional tracking (Figures 1, 2 and 3).ResultsFindings showed a global improvement after the VR Zen Garden App session, mainly in reducing levels of anxiety (Cohen’s d= 1.07) and promoting relaxation (Cohen’s d= 0.95), with possible applications especially before and after meals when food fears are at their highest. In addition, the music that was played during the intervention had a particularly positive effect.Image:Image 2:Image 3:ConclusionsDespite limitations, such us the small sample size and the one time point measurement, positive clinical implications have been highlighted for the Zen Garden VR App in patients with AN, although further studies are needed to confirm these preliminary findings. It is possible that VR could usefully augment and personalize care for people with an EDs. A range of interventions might be used to target the most compromised symptoms such as designing interventions that can help with triggers to eating disorder psychopathology.Disclosure of InterestNone Declared

Interplay of Environmental Factors, Genetic Susceptibility, and Sleep Disturbances predict Bipolar Disorder’s Relapses: preliminary results from a pilot study

IntroductionPredicting acute affective episodes in individuals with Bipolar Disorder (BD) remains a clinical challenge. Specific environmental stressors, including air pollution, noise, and temperature variations might worsen affective symptoms or sleep in the general population, but their role in BD relapses is often overlooked. Indeed, they might exacerbate BD by perturbing circadian rhythms – fundamental aspects of BD.ObjectivesWe thereby present the protocol of this pilot study and future preliminary data. We aim to longitudinally assess sleep alterations, mood fluctuations, and environmental exposure to several factors (air pollutants, climate, noise, artificial light-at-night, green space access) in patients with BD and to check the association of these variables with BD relapses.MethodsIn this pilot study, we will recruit 40 patients with BD in a 6-month prospective study. Patients were assessed during baseline, at 3 and 6 months. Data recollected will consist of a subjective (questionnaires) and objective (through meteorological stations) evaluation of physical environmental factors around the home residence; clinical assessment of mood and circadian rhythms, and continuous tracking of sleep-wake patterns, energy, and movement using actigraphy.ResultsExpected results will show that exposure to a worse environment (higher pollution, noise, light exposure, climate) will be associated with worse BD outcomes (i.e., relapse, mood symptoms, sleep alterations).ConclusionsWe will be sharing preliminary data from our ongoing study, offering insights into early patterns and findings that shed light on our objectives.Disclosure of InterestNone Declared

A review of wearable technology in healthcare: Monitoring patient health and enhancing outcomes

Wearable technology has emerged as a transformative force in the healthcare landscape, reshaping the way patient health is monitored and healthcare outcomes are enhanced. The paper delves into various types of wearables, including fitness trackers, smartwatches, and medical wearables, elucidating their functionalities and applications in the healthcare domain. The review emphasizes the profound influence of wearable technology on patient monitoring, encompassing continuous health tracking, remote patient monitoring, and chronic disease management. Through real-time data collection and analysis, wearables enable early detection of health abnormalities and empower healthcare providers to offer timely interventions, thereby reducing hospital readmissions. The role of wearables in chronic disease management is examined, showcasing their potential in tailoring personalized treatment plans and facilitating proactive health management. Furthermore, the paper explores the broader implications of wearable technology in enhancing healthcare outcomes. Wearables contribute to improved patient engagement by providing individuals with access to their health data, fostering a sense of empowerment and accountability. The review underscores the shift towards personalized healthcare, where wearables play a pivotal role in tailoring treatment plans based on individual health data and leveraging predictive analytics for disease prevention. Despite the promising advancements, challenges such as data security, privacy concerns, and integration with existing healthcare systems are discussed. Strategies to address these challenges and enhance user adoption and adherence to wearable technology are explored, recognizing the importance of a holistic approach in realizing the full potential of wearables in healthcare. This review serves as a comprehensive guide for researchers, healthcare providers, and policymakers, offering insights into the evolving landscape of wearable technology and its profound impact on monitoring patient health and improving healthcare outcomes.

Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

Long-Term Tumor-Targeting Effect of E. coli as a Drug Delivery System.

To overcome the limitations of current nano/micro-scale drug delivery systems, an Escherichia coli (E. coli)-based drug delivery system could be a potential alternative, and an effective tumor-targeting delivery system can be developed by attempting to perform chemical binding to the primary amine group of a cell membrane protein. In addition, positron emission tomography (PET) is a representative non-invasive imaging technology and is actively used in the field of drug delivery along with radioisotopes capable of long-term tracking, such as zirconium-89 (89Zr). The membrane proteins were labeled with 89Zr using chelate (DFO), and not only was the long-term biodistribution in tumors and major organs evaluated in the body, but the labeling stability of 89Zr conjugated to the membrane proteins was also evaluated through continuous tracking. E. coli accumulated at high levels in the tumor within 5 min (initial time) after tail intravenous injection, and when observed after 6 days, 89Zr-DFO on the surface of E. coli was found to be stable for a long period of time in the body. In this study, we demonstrated the long-term biodistribution and tumor-targeting effect of an E. coli-based drug delivery system and verified the in vivo stability of radioisotopes labeled on the surface of E. coli.

Spacecraft Formation Keeping and Reconfiguration Using Optimal Visual Servoing

This paper proposes a direct visual servoing system for spacecraft guidance in formation flying scenarios. The proposed image-based visual servoing system uses image information for planning and executing formation acquisition, reconfiguration, and maintenance maneuvers. The system assumes that LEDs are located at specific points on the satellites, enabling the visual servoing controller to rely on continuous tracking of these features in the camera’s image plane. Analytical developments demonstrate that the proposed optimal visual control system is stable and optimal, and it acts on both the orbital and attitude dynamics of the spacecraft, considering circular and elliptical reference orbits. The distributed image-based controller defines a cost function that minimizes control efforts, and the paper proses an optimal framework for developing controllers that address the issue. A ROS-based simulation tool was used to test the proposed visual servoing controller in a realistic small-sat formation flying scenario. Results indicate that the proposed distributed control strategy is viable and robust against environmental perturbations and disturbances in sensing and actuation.

Semi-automated computer vision-based tracking of multiple industrial entities: a framework and dataset creation approach

This contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e.g., pallets, crates, barrels) over a network of, in this example, six RGB cameras. This framework makes use of multiple sensors, data pipelines, and data annotation procedures, and is described in detail in this contribution. With the vision of a fully automated tracking system for industrial entities in mind, it enables researchers to efficiently capture high-quality data in an industrial setting. Using this framework, an image dataset, the TOMIE dataset, is created, which at the same time is used to gauge the framework’s validity. This dataset contains annotation files for 112,860 frames and 640,936 entity instances that are captured from a set of six cameras that perceive a large indoor space. This dataset out-scales comparable datasets by a factor of four and is made up of scenarios, drawn from industrial applications from the sector of warehousing. Three tracking algorithms, namely ByteTrack, Bot-Sort, and SiamMOT, are applied to this dataset, serving as a proof-of-concept and providing tracking results that are comparable to the state of the art.

Solar PV tracking system using arithmetic optimization with dual axis and sensor

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

New Eldercare Robot with Path-Planning and Fall-Detection Capabilities

The rapid growth of the elderly population has led to an increased demand for effective and personalized eldercare solutions. In this paper, the design and development of an eldercare robot is presented. This robot is specifically tailored to meet the two specific challenges faced by the elderly. The first is the continuous indoor tracking of the elder, while the second is the fall detection. A comprehensive overview of the hardware and software components, as well as the control architecture of the robot is presented. The hardware design of the robot incorporates a range of features, including a perception system comprising a 2D Lidar, IMU, and camera for environment mapping, localization, and fall detection. The software stack of the robot is explained as consisting of layers for perception, mapping, and localization. The robot is tested experimentally to validate its path planning capability by using Hector SLAM and the RRT* technique. Experimental path planning has shown a positioning accuracy of 93.8% on average. Elderly fall detection is achieved by using the YOLOv7 algorithm at a percentage of 96%. Experimental results have been discussed and evaluated.

From Surveying to Surveillance: Maritime Cartography and Naval (Self-)Tracking in the Long Nineteenth Century

Abstract In the eighteenth century, ‘ship tracks’, lines recording vessels’ movements on charts, facilitated wayfinding, hydrographical surveys and territorial claims. During the long nineteenth century, however, their main function shifted from surveying of the marine environment to surveillance of officers’ movements and actions. Using textual and cartographical sources produced by British naval officers, this article argues that geosurveillance and the continuous visual tracking of individuals with reference to mapping systems were developed at sea, long before the aerial and digital revolutions, and independently of panoptical models. In the nineteenth century, most cartographical tracking was disciplined self-tracking, actively performed by the surveilled themselves. This required their employers (notably the state) to emphasize honour, training, conscientiousness and procedure. The Admiralty used tracks for testing performance, verifying accounts, establishing responsibilities and co-ordinating movement. Monitoring individuals through their record was the natural inverse of a pattern discussed by historians of science: data verification through authorial ‘credibility’. The two-way bond between the surveilled and their track was eventually broken in the twentieth century by technological innovations that allowed external and non-consensual geo-tracking. This changed the import of surveillance, discipline and the sea itself, no longer a space where human movement would be inevitably lost from sight.

Full-Size Experimental Measurement of Combustion and Destruction Efficiency in Upstream Flares and the Implications for Control of Methane Emissions from Oil and Gas Production

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability.

Fabricating Solid‐Solution‐Type Perovskite/Fluoride Nanocomposites Through Sublattice Interlocking for High‐Performance Photovoltaics

AbstractAll‐inorganic α‐phase CsPbI3 perovskite with a suitable bandgap and superb optoelectronic properties is transforming the landscape of perovskite photovoltaics, but its long‐term lability associated with “soft” ionic lattice still imposes a great challenge for practical applications. Herein, a unique solid‐solution fluorination strategy is proposed to deliver an “ideal” perovskite matrix of α‐phase CsPbI3 abundant with F ions through interlocking the soft lattice of CsPbI3 with cubic‐phase CsF·3/2HF. Such a sublattice interlocking can not only stabilize the soft lattice of α‐phase CsPbI3 perovskite nanocrystals but also passivate their notorious surface defects, thereby producing a “rigid” solid‐solution‐type perovskite/fluoride CsPbI3/CsF (termed as CsPbI3:F) nanocomposite with excellent long‐term stability and a near‐unity photoluminescence efficiency. Of particular note is that these CsPbI3:F nanocomposites can work well as effective grain boundary anchors to significantly improve the photovoltaic performance of perovskite solar cells because of their F‐rich perovskite lattice, achieving a T80 stability of 1500 h under continuous maximum power point tracking and AM 1.5G illumination without the need for encapsulation. This work paves a new way to deliver perovskite materials with desirable properties for photovoltaics.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave (FMCW) radar is an advantageous sensor scheme for target estimation and environmental perception. However, existing algorithms based on discrete Fourier transform (DFT), multiple signal classification (MUSIC) and compressed sensing, etc., cannot achieve both low complexity and high resolution simultaneously. This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar. Firstly, we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy. Secondly, we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data, which improves the efficiency of continuous target tracking. These two approaches have improved the algorithm efficiency by nearly 2–4 orders of magnitude without losing accuracy and resolution. Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Accuracy and reliability of wristband smartwatch-based estimation of circadian rhythm of heart rate in stroke patients

Background & Objective: Stroke has a high incidence, disability, and mortality rate, which has a serious impact on the patients’ quality of life. Heart rate (HR) is valuable in the prognostic assessment of stroke patients. Continuous HR tracking helps to detect abnormal indicators and provide interventions timely to avoid disease progression. However, the cuff-based or electrode-based HR monitoring tools commonly used in clinic are not suitable for continuous monitoring in daily life. The aim of this study was to verify the accuracy and reliability of a wristband smartwatch in monitoring HR over 24-hour in stroke patients, including in terms of the circadian rhythm characteristics of HR. Methods: 25 stroke patients underwent 24-hour HR monitoring by wearing a smartwatch and a clinical monitor. We use ambulatory blood pressure monitors and bedside monitors as the gold standard for clinical HR monitoring. The degree of association between the two measurements was compared using Pearson’s correlation coefficient. Bland-Altman analysis showed the accuracy of the smartwatch in HR monitoring. Intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to verify the reliability of the wristband smartwatch for repeated measurements. Results: A total of 625 heart rate pairs were acquired. The smartwatches had an average error of 0.816 bpm in 24-hour, with no difference from standard monitors (P>0.05), indicating good accuracy. The intraday repeated measures ICC was 0.969 and the interday repeated measures ICC was 0.788, indicating good reliability. Conclusions: The smartwatch is acceptable in detecting 24-hour ambulatory HR in stroke patients, and in the future, long-term HR monitoring with diurnal variations can be performed in daily life.

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Region-based convolutional neural networks for occluded person re-identification

In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to detect occluded persons at different levels of occlusion before forwarding the image for the re-ID process. Three occluded-specific datasets are selected to evaluate the model's effectiveness in detecting occluded people. There are 379 distinct people in total, and each has five images obstructed from different angles. A sample of the data is taken to simulate various environment settings, and new data points are generated with different degrees of occlusion to assess how well the model performs under varying levels of obstruction. The findings demonstrate that the proposed person re-ID model is reliable in most circumstances, correctly re-identifying at 74% (Rank-1) and 90% (Rank-5). Although there is a decrease in accuracy as the number of distinctive people in the dataset increases, this does not significantly impact the tracking performance in various applications, which are expected to recognize a single person or a small group of individuals. Future works will explore refining similarity matching algorithms by delving into robust image comparison techniques, thereby addressing the challenges presented by occlusions. A critical aspect is to assess the model under diverse lighting conditions and investigate scenarios with multiple individuals in a frame. It is also beneficial to exploit high-resolution datasets, such as DukeMTMC-reID, and integrate finer contextual details, like clothing or carried objects. These collective efforts are essential for optimizing the model’s efficacy in practical applications and advancing person re-ID technologies.