Human Tracking Research Articles

A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information.

The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and a new machine learning (ML) method called NKCK. The NKCK method can be broken down into three components: neighborhood component analysis (NCA) for dimensionality reduction, K-means clustering, and K-nearest neighbor (KNN) classification with cross-validation (CV). The KNN algorithm is particularly suitable for our dataset since it effectively classifies data based on proximity, relying on the spatial relationships between points. Experimental results indicate that the NKCK method outperforms traditional methods, achieving reductions in error rates of 82.4% compared to naive Bayes (NB), 85.0% compared to random forest (RF), 72.1% compared to support vector machine (SVM), 64.7% compared to multilayer perceptron (MLP), 50.0% compared to density-based spatial clustering of applications with noise (DBSCAN)-based methods, 42.0% compared to linear discriminant analysis (LDA)-based channel state information (CSI) amplitude fingerprinting, and 33.0% compared to principal component analysis (PCA)-based approaches. Due to the sensitivity of CSI, our multi-user online positioning system faces challenges in detecting dynamic human activities, such as human tracking, which requires further investigation in the future.

A Neuromorphic Radar Sensor for Low-Power IoT Systems

As radar sensors become an integral component of Internet of Things (IoT) systems, the challenge of high power consumption poses a significant barrier, especially for battery-operated devices. This article introduces NeuroRadar, a groundbreaking solution that leverages a radar front-end capable of generating spike sequences, which can be efficiently processed by energy-saving Spiking Neural Networks (SNNs). We explore the innovative design and implementation of NeuroRadar, showcasing its effectiveness in applications like gesture recognition and human tracking. By achieving dramatically lower power consumption compared to traditional radar systems, NeuroRadar represents a new paradigm in energy-efficient IoT sensing.

WiShield: Privacy Against Wi-Fi Human Tracking

WiShield: Privacy Against Wi-Fi Human Tracking

WiVelo: Fine-grained Wi-Fi Walking Velocity Estimation

Passive human tracking using Wi-Fi has been researched broadly in the past decade. Besides straightforward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS), which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this article, we present WiVelo, which explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas’ locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90-percentile tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-art. The datasets and source codes are published through Github ( https://github.com/research-source/code ).

Design and implementation of deep learning-based object detection and tracking system

Many human tracking methods by deep learning rely on powerful computing resources. For embedded platforms with limited resources, efficient use of resources is a priority. In this paper, we design an object detection and tracking system based on deep learning methods. We propose an efficient system with software and hardware design. We apply the framework of Vitis AI and its Deep Learning Processing Unit using a hardware/software co-design approach. This approach capitalizes on a higher-level acceleration design framework, where the convolutional models can be updated more flexibly and rapidly. This design approach not only provides a fast design flow but also has good performance in terms of throughput. We facilitate the design and accelerate the object detection model YOLO v3 to achieve higher throughput and energy efficiency. Our tracking method achieves a 1.27x improvement in processing speed with the addition of a single-object tracker. Our proposed human tracking methods can achieve better performance than the others in precision with the same test sequences.

Scaling up time–geographic computation for movement interaction analysis

AbstractUnderstanding interactions through movement provides critical insights into urban dynamic, social networks, and wildlife behaviors. With widespread tracking of humans, vehicles, and animals, there is an abundance of large and high‐resolution movement data sets. However, there is a gap in efficient GIS tools for analyzing and contextualizing movement patterns using large movement datasets. In particular, tracing space–time interactions among a group of moving individuals is a computationally demanding task, which would uncover insights into collective behaviors across systems. This article develops a Spark‐based geo‐computational framework through the integration of Esri's ArcGIS GeoAnalytics Engine and Python to optimize the computation of time geography for scaling up movement interaction analysis. The computational framework is then tested using a case study on migratory turkey vultures with over 2 million GPS tracking points across 20 years. The outcomes indicate a drastic reduction in interaction detection time from 14 days to 6 hours, demonstrating a remarkable increase in computational efficiency. This work contributes to advancing GIS computational capabilities in movement analysis, highlighting the potential of GeoAnalytics Engine in processing large spatiotemporal datasets.

Design an Effective, Faster Region-Based Convolutional Neural Network with Optimization for the Human Tracking System

Nowadays, real-time Human Tracking System (HTS) is a crucial topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality, among many others. In this paper, we design a Faster Region-based Convolutional Neural Network (FR-CNN) with Crow Search Optimization (FR-CNN-CSO) architecture to improve computational complexity and enhance the performance of HTS. The system is implemented in a Python environment with video input. Remove unnecessary data from the gathered datasets during preprocessing. Next, feature extraction is processed using Histograms of Oriented Gradients (HOG). Then update, the extracted features into a designed FR-CNN model for identifying and tracking a person using crow search fitness. The main goal of the developed approach is to attain accurate prediction results and improve the computational complexity by achieving less execution time. Finally, the experimental outcomes show the reliability of the designed system by other conventional techniques in terms of accuracy, precision, recall, F-measure, and execution time.

Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit

The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.

Deep learning-based human pose estimation towards artworks classification

ABSTRACT Human pose detection has attracted more attention in recent years, particularly with various applications such as human-computer interaction, motion recognition, action prediction, gaming, sign language translation, video surveillance and human tracking. For example, it is challenging to analyse the proportions of the human body in historical artwork collections for classifying genres, styles, and artists. Unfortunately, most of existing detection methods do not generalize well across artworks, resulting in poorly recognized differences in the proportions. Therefore, we present a large-scale analysis of 130,000+ paintings and ninety-nine human pose estimation (HPE) methods to show that different artistic styles have a distinct average degree of human proportions. We have further verified that the Topdown Heatmap + Scnet algorithm with a threshold of 0.3 can classify artworks effectively and fully distinguish historical epochs. This analysis is a baseline for researchers to discover new techniques towards finding the canon of art based on symmetry and proportions.

Embracing Distributed Acoustic Sensing in Car Cabin for Children Presence Detection

Contactless acoustic sensing has been actively exploited in the past few years to enable a large range of applications, ranging from fine-grained vital sign monitoring to coarse-grained human tracking. However, existing acoustic sensing systems mainly work on smartphone or smart speaker platforms. In this paper, we envision an exciting new acoustic sensing platform, i.e., car cabin which is inherently embedded with a large number of speakers and microphones. We propose the new concept of distributed acoustic sensing and develop novel designs leveraging the unique characteristics of rich multi-path in car cabin to enable fine-grained sensing even when the primary reflection is totally blocked. By using child presence detection as the application example, we show that child presence can be detected through body motions or even subtle breath (when the child is sleeping or in coma) at all locations in the cabin without any blind spots. We further show that the proposed system can robustly work in different car cabins, achieving an average detection accuracy of 97% and a false alarm rate always below 2% under different scenarios including those challenging ones such as rear-facing seat blockage. We believe the proposed distributed sensing modality in car cabin pushes acoustic sensing one big step towards real-life adoption.

Annual Meeting Humanities Track Looks at Psychiatry as an Art

Annual Meeting Humanities Track Looks at Psychiatry as an Art

Evolution of CNNs in human tracking applications

Tracking the movement of objects in videos or footage from CCTV systems plays an integral role in crime investigations, surveillance, security predictions, and many other domains. Historically, this task was primarily entrusted to dedicated observers or analysts who would be summoned to review pre-recorded footage post-event. With the advent of machine learning and AI, convolutional neural networks (CNNs) have paved the way for computers to augment human capabilities in analyzing streaming videos or archived recordings. Among the various tracking methodologies that machine learning offers, YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Networks), along with its iterations, stand out as some of the most reliable and precise. However, the scope of analysis often extends beyond these techniques. To enhance accuracy and provide an adept classification mechanism, the Deep SORT (Simple Online and Realtime Tracking) algorithm emerges as pivotal. Its synergy with human detection remains a significant area of discussion and will be deliberated upon in this study. This review aims to elucidate the intricacies of these state-of-the-art methods and their interplay in modern tracking systems.

InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction from Multi-view RGB-D Images

Humans constantly interact with objects to accomplish tasks. To understand such interactions, computers need to reconstruct these in 3D from images of whole bodies manipulating objects, e.g., for grasping, moving and using the latter. This involves key challenges, such as occlusion between the body and objects, motion blur, depth ambiguities, and the low image resolution of hands and graspable object parts. To make the problem tractable, the community has followed a divide-and-conquer approach, focusing either only on interacting hands, ignoring the body, or on interacting bodies, ignoring the hands. However, these are only parts of the problem. On the contrary, recent work focuses on the whole problem. The GRAB dataset addresses whole-body interaction with dexterous hands but captures motion via markers and lacks video, while the BEHAVE dataset captures video of body-object interaction but lacks hand detail. We address the limitations of prior work with InterCap, a novel method that reconstructs interacting whole-bodies and objects from multi-view RGB-D data, using the parametric whole-body SMPL-X model and known object meshes. To tackle the above challenges, InterCap uses two key observations: (i) Contact between the body and object can be used to improve the pose estimation of both. (ii) Consumer-level Azure Kinect cameras let us set up a simple and flexible multi-view RGB-D system for reducing occlusions, with spatially calibrated and temporally synchronized cameras. With our InterCap method we capture the InterCap dataset, which contains 10 subjects (5 males and 5 females) interacting with 10 daily objects of various sizes and affordances, including contact with the hands or feet. To this end, we introduce a new data-driven hand motion prior, as well as explore simple ways for automatic contact detection based on 2D and 3D cues. In total, InterCap has 223 RGB-D videos, resulting in 67,357 multi-view frames, each containing 6 RGB-D images, paired with pseudo ground-truth 3D body and object meshes. Our InterCap method and dataset fill an important gap in the literature and support many research directions. Data and code are available at https://intercap.is.tue.mpg.de.

Designing A Proposed Educational Program Based on Geographical Cloud Computing and Measuring its Effectiveness in Developing Research and Analysis Skills for Remote Sensing Big Data for Spatial Phenomena among Humanities Track Students at the Secondary Stage in Makkah

Designing A Proposed Educational Program Based on Geographical Cloud Computing and Measuring its Effectiveness in Developing Research and Analysis Skills for Remote Sensing Big Data for Spatial Phenomena among Humanities Track Students at the Secondary Stage in Makkah

Technical note: A volumetric method for measuring the longitudinal arch of human tracks and feet.

Fossil footprints (i.e., tracks) were believed to document arch anatomical evolution, although our recent work has shown that track arches record foot kinematics instead. Analyses of track arches can thereby inform the evolution of human locomotion, although quantifying this 3-D aspect of track morphology is difficult. Here, we present a volumetric method for measuring the arches of 3-D models of human tracks and feet, using both Autodesk Maya and Blender software. The method involves generation of a 3-D object that represents the space beneath the longitudinal arch, and measurement of that arch object's geometry and spatial orientation. We provide relevant tools and guidance for users to apply this technique to their own data. We present three case studies to demonstrate potential applications. These include, (1) measuring the arches of static and dynamic human feet, (2) comparing the arches of human tracks with the arches of the feet that made them, and (3) direct comparisons of human track and foot arch morphology throughout simulated track formation. The volumetric measurement tool proved robust for measuring 3-D models of human tracks and feet, in static and dynamic contexts. This tool enables researchers to quantitatively compare arches of fossil hominin tracks, in order to derive biomechanical interpretations from them, and/or offers a different approach for quantifying foot morphology in living humans.

3D convolutional long short-term encoder-decoder network for moving object segmentation

Moving object segmentation (MOS) is one of the important and wellstudied computer vision tasks that is used in a variety of applications, such as video surveillance systems, human tracking, self-driving cars, and video compression. While traditional approaches to MOS rely on hand-crafted features or background modeling, deep learning methods using Convolution Neural Networks (CNNs) have been shown to be more effective in extracting features and achieving better accuracy. However, most deep learning-based methods for MOS offer scene-dependent solutions, leading to reduced performance when tested on previously unseen video content. Because spatial features are insufficient to represent the motion information, the spatial and temporal features should be used together to succeed in unseen videos. To address this issue, we propose the MOS-Net deep framework, an encoder-decoder network that combines spatial and temporal features using the flux tensor algorithm, 3D CNNs, and ConvLSTM in its different variants. MOS-Net 2.0 is an enhanced version of the base MOS-Net structure, where additional ConvLSTM modules are added to 3D CNNs for extracting long-term spatiotemporal features. In the final stage of the framework the output of the encoder-decoder network, the foreground probability map, is thresholded for producing a binary mask where moving objects are in the foreground and the rest forms the background. In addition, an ablation study has been conducted to evaluate different combinations as inputs to the proposed network, using the ChangeDetection2014 (CDnet2014) which includes challenging videos such as those with dynamic backgrounds, bad weather, and illumination changes. In most approaches, the training and test strategy are not announced, making it difficult to compare the algorithm results. In addition, the proposed method can be evaluated differently as video-optimized or video-agnostic. In video-optimized approaches, the training and test set is obtained randomly and separated from the overall dataset. The results of the proposed method are compared with competitive methods from the literature using the same evaluation strategy. It has been observed that the introduced MOS networks give highly competitive results on the CDnet2014 dataset. The source code for the simulations provided in this work is available online.

A Virtual Mecanum Wheeled Robot ROS Simulator for Multi-view and Self-Following Motion Capture

Motion capture (Mocap) on the go based on a mobile platform is valuable for clinical studies and rehabilitation. For multi-view gait analysis, Mecanum wheeled robots offer advantages over traditional differential drive robots. However, control issues in multi-view human tracking using Mecanum robots remain unexplored and lack a suitable virtual environment. This paper introduces a virtual Robot Operating System (ROS) environment with a Gazebo simulator as a research tool for multi-view human tracking on a Mecanum wheeled robot. The simulation incorporates a Proportional–Integral–Derivative (PID) controller and Kalman filter to maintain expected positional distance and relative viewing angles to the target. Our case study presents a quantitative evaluation of results obtained from the virtual environment for two specific tracking modes on a Mecanum wheeled robot: back-view following and side-view following with and without Kalman filtering. By optimizing the system, we decreased the distance error in backward following from 0.22 m to 0.12 m, and the angle error from 12.2° to 5.3°. Similarly, for side following, the distance error decreased from 0.32 m to 0.14 m, and the angle error reduced from 13.4° to 6.2°. These experimental results demonstrate that our approach enhances the accuracy of both tracking methods by over 50%. This work provides a necessary steppingstone for the development of human-tracking Mecanum wheeled robots for use in a clinical setting, providing a virtual environment for algorithmic development thereby eliminating wear on the hardware to be used in the clinical setting.

Passive Human Tracking With WiFi Point Clouds

Passive Human Tracking With WiFi Point Clouds

Device-free Human Tracking and Gait Recognition Based on the Smart Speaker

Device-free Human Tracking and Gait Recognition Based on the Smart Speaker

Real-Time Human Tracking Using Multi-Features Visual With CNN-LSTM and Q-Learning

Real-Time Human Tracking Using Multi-Features Visual With CNN-LSTM and Q-Learning