Mobile Object Tracking Research Articles

GLAC: High-Precision Tracking of Mobile Objects With COTS RFID Systems

GLAC: High-Precision Tracking of Mobile Objects With COTS RFID Systems

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

Edge computing-Based mobile object tracking in internet of things

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

Swarm-Based and Energy-Aware Unmanned Aerial Vehicle System for Video Delivery of Mobile Objects

Floods are the most common disaster in the world and cause deaths, damages to houses, buildings, and possessions, as well as disruption to communications. In such dynamic scenarios, it is needed to search and track mobile objects transported by the water flows, such as humans, animals, vehicles, and debris. The use of Unmanned Aerial Vehicles (UAVs) is essential in disaster scenarios to help first responders determine correct procedures in terms of searching, tracking, and rescuing the victims, as well as in defining the actions to minimize the risks in a sustainable and timely manner. However, the tracking of mobile objects and the delivery of real-time video sequences from UAVs with energy constraints toward first responders is still a hard task. Therefore, it is necessary to orchestrate a swarm of UAVs for searching and tracking objects while reacting fast to frequent changes in the topology and trajectory, as well as distributing real-time video flows with quality level support to the ground team. This article proposes an energy-aware swarm-based and mobility prediction scheme for UAVs, called SUAV. SUAV provides a unique UAV-based system for rescue scenarios with mobile objects and with support for route and path planning, searching and tracking procedures, mobility prediction and multi-hop communication management, video delivery with Quality of Service (QoS)/Quality of Experience (QoE) support, and energy-efficiency. Simulation results show the efficiency of SUAV in transmitting real-time video flows with QoS/QoE support while minimizing the energy consumption in flooding scenarios with mobile targets.

ISense: Intelligent Object Sensing and Robot Tracking Through Networked Coupled Magnetic Resonant Coils

Object sensing and tracking using electric and magnetic fields allow intelligent interaction, automation, and adaptation in cyber–physical systems. Our approach, called iSense, uses a software-defined collaborative sensing technique for the detection of the type of object when placed on a large surface and tracking its mobility. iSense is cost effective, low power, and scalable, which allows its use over large surfaces. First, we introduce a dual-coil magnetic resonant sensing architecture based on nested coils, i.e., passive (outer) and active (inner) coils, for low-power contactless sensing. Second, a data-driven support vector machine-based approach helps to classify different types of objects using the voltage readings obtained at the passive coil. iSense combines sensed voltage information from multiple different coils spread over the surface with a group-based interference mitigation mechanism between coils for collaborative sensing. We validate our system with real-time prototype and experimental evaluations. We demonstrate the detection of seven different types of objects over three different materials, and real-time detection and tracking of mobile objects including a robot car. Experimental results show that each sensing coil only consumes few milliwatts, i.e., $18\times $ less than inductive sensing and $15\times $ less than classical magnetic resonance sensing, extend sensing depth to 3 cm, and enable tracking on the large surface sensing with more than 90% accuracy for velocity estimation.

A chaos-enhanced accelerated PSO algorithm in reliable tracking of mobile objects

Object tracking in monitoring applications is one of the topics in Internet of Things (IoT) issues. Most important key challenges in object tracking have concentrated on energy consumption and service quality and reliability. In the proposed approach the good performance of the PSO algorithm in global optimisation, and its weakness in local optimisation, is combined with the chaos operator to overcome this flaw. The purpose of the present research is to improve energy consumption and service quality and increase system reliability in mobile object tracking in WSNs. The goal is also to improve the algorithm performance. According to the results of the simulations, the energy consumption in the proposed method has been improved due to the optimal selection of the cluster heads. Also, the proposed method is stable while improving the reliability and increasing the quality of sensor network services.

A chaos-enhanced accelerated PSO algorithm in reliable tracking of mobile objects

A chaos-enhanced accelerated PSO algorithm in reliable tracking of mobile objects

Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects

We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.

Object Tracking Over Distributed WSNs With Consensus on Estimates and Missing Data

Wireless sensor network (WSN) technologies are used to provide mobile object tracking due to advantages such as mobility, scalability, and flexibility. However, wireless interaction between the network nodes is often accompanied by missing data, which requires robustness from the estimator. This paper develops an iterative distributed unbiased finite impulse response (dUFIR) filtering algorithm for object tracking via WSNs with consensus on estimates and shows that it has higher robustness than the distributed Kalman filter (dKF). The tracking problem is viewed as a real-time position estimation of an unmanned ground vehicle (UGV). The extensive simulations are provided using real sensor parameters and measurements of the UGV position with missing data. Two different scenarios are considered when: 1) each sensor is capable of measuring the UGV position and 2) sensors have different time-varying noise variances, as in practical WSNs. The higher robustness of the dUFIR against the dKF is demonstrated under diverse operation conditions.

PRTS: A Passive RFID Real-Time Tracking System Under the Conditions of Sparse Measurements

In many logistics and manufacturing applications, tracking of moving objects with radio frequency identification (RFID) tags on a conveyor belt is a premise for many other processes, e.g., sorting procedures, stamping IDs in a surveillance video. However, in complex industrial environments, a sharp decline in Tag Read Record (TRR) often results in severe spatial ambiguity. In this type of scenarios, existing systems cannot work effectively owing to the prevalent existence of noise. This paper proposes a Passive RFID Real-time Tracking System (PRTS) with tolerance of a small TRR, which is designed for tracking RFID-tagged mobile objects. We use detailed deduction to convert the tracking problem into a sparse signal reconstruction one. To solve this problem, we devise a novel normal sparse signal reconstruction method based on greedy pursuit by making the best of the available prior knowledge and further ameliorate it via calibration of the phase deviation from frequency and angle-of-arrival responses. Furthermore, we leverage the simplified particle filters to facilitate the real-time tracking of mobile objects on conveyor belts. We implement the prototype PRTS with commercial-off-the-shelf RFID devices and evaluate it in various scenarios. Experimental results demonstrate that PRTS achieves a mean relative error of 7 cm under the conditions of extremely sparse measurements.

Secure and reliable object tracking in wireless sensor networks

Mobile object tracking is one of the most important applications of Wireless Sensor Networks (WSNs) deployed in battlefields, wildlife or habitat monitoring applications. Existing object tracking algorithms are mostly centralized and based on heavy and complex signal processing algorithms, hence they cannot be applied to resource constrained WSNs directly. Object tracking algorithms of WSNs should be designed by considering energy conservation, bandwidth and communication overheads. Moreover, as practical object tracking applications are typically used in mission-critical applications, security is another important design matter to be considered. In mission-critical applications, sensor nodes are deployed in hostile fields and they can be easily captured by intruders. Such compromised nodes can be used to falsify the collected data and threaten the object tracking reliability. In this paper, we propose a novel secure and reliable object tracking protocol that considers security and object tracking tasks simultaneously. The basic idea behind the proposed protocol is to ensure tracking security using reputation based trust concept for individual sensor nodes. The performance evaluation results show that the proposed protocol allows the network to retain the reliability of tracking data even in the presence of compromised nodes, thereby achieving secure and reliable object tracking process.

A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation.

Tunnel construction workers face many dangers while working under dark conditions, with difficult access and egress, and many potential hazards. To enhance safety at tunnel construction sites, low latency tracking of mobile objects (e.g., heavy-duty equipment) and construction workers is critical for managing the dangerous construction environment. Wireless Sensor Networks (WSNs) are the basis for a widely used technology for monitoring the environment because of their energy-efficiency and scalability. However, their use involves an inherent point-to-point delay caused by duty cycling mechanisms that can result in a significant rise in the delivery latency for tracking mobile objects. To overcome this issue, we proposed a mobility-aware adaptive duty cycling mechanism for the WSNs based on object mobility. For the evaluation, we tested this mechanism for mobile object tracking at a tunnel excavation site. The evaluation results showed that the proposed mechanism could track mobile objects with low latency while they were moving, and could reduce energy consumption by increasing sleep time while the objects were immobile.

Hierarchical Location Caching Scheme for Mobile Object Tracking in the Internet of Things

Hierarchical Location Caching Scheme for Mobile Object Tracking in the Internet of Things

Indoor mobile object tracking using RFID

Indoor tracking has been a challenging task compared to outdoor cases provided by GPS and a variety of ranging-based solutions. In this work, we propose a promising approach using RFID for indoor mobile object tracking. A moving object equipped with an RFID tag can be tracked by the pre-deployed RFID reader network. It is in the interest of many scenarios due to its low cost, ease of deployment and good performance in indoor settings. However, the identification of a large scale of tags that surround RFID readers within the limited time raises a big challenge for RFID-based tracking. To solve this problem, we propose two core design components, RFID MAC and a range-free tracking algorithm. In MAC layer, we develop two protocols, one is Framed Slotted Aloha (random access) and the other is Tree-Walking (polling), as the underlying sensing component. We also develop a basic tracking on a central node which collects tag identification information from all RFID readers. Our system is implemented in the Qualnet tool, and we run numerous experiments to study performance under different parameter settings. These results validate the effectiveness of our indoor tracking solution using RFID.

Distributed processing of big mobility data as spatio-temporal data streams

Recent rapid development of wireless communication, mobile computing, global navigation satellite systems (GNSS), and spatially enabled sensors are leading to an exponential growth of available mobility data produced continuously at high speed. Due to these advancements, a new class of monitoring applications has come to the focus, including real-time intelligent transportation systems, traffic monitoring and mobile objects tracking. These new information flow processing (IFP) application domains need to process huge volume of mobility data arriving in the form of continuous data streams from mobile objects. IFP applications are pushing traditional database technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. Mobility data, i.e. real-time, transient, time-varying sequences of spatio-temporal data items, generated by embedded positioning sensors demonstrates at least two Big Data core features: volume and velocity. Existing distributed data stream management systems (DSMS), real-time computing systems (RTCS) and their processing models are dominantly based on relational paradigm and continuous operator model. Thus, they have rudimentary spatio-temporal capabilities, provide expensive fault recovery requiring either hot replication or long recovery times, and do not handle faults and slow nodes. The framework proposed in this paper is a cornerstone towards efficient real-time managing and monitoring of mobile objects through distributed spatio-temporal streams processing on large clusters. A prototype implementation is rooted in a new stream processing model that overcomes the challenges of current distributed stream processing models and enable seamless integration with batch and interactive processing like MapReduce.

Location Tracking based on MS-Based/Assisted Location Trigger Model with Context-Awareness

In this paper, we proposed the location tracking system based on MS-Based/Assisted(Mobile Station-Based and Assisted) location trigger service model with context-awareness for the intelligent location tracking of moving objects. It provides the proper resulting value that matches the context of users through the analysis about the situation of the user, physical environment, computing resource and the existing information on user input. In order to provide real-time data, we proposed the location tracking system which realizes the intelligent information such as the expecting arrival time and passing the specific area of the moving object by adopting the location trigger. So, it derives to minimize the costs of communication for the mobile object tracking applications. The proposed location tracking system based on context-awareness can be used for realtime monitoring, intelligent alarm/action, setting up of the optimized moving path, dynamic adjustment of strategies and policies. So it has the advantage to develop the application system which is aimed at optimization of the object tracking and movement.

A Mobile Object Tracking Scheme by Wired/wireless Integrated Street Lights with RFID

Since a sophisticated location determination technology (LDT) is necessary for accurate positioning in urban area environments, numerous studies related to the LDT using the RFID (Radio Frequency IDentification) technology have been implemented for real‐time positioning and data transferring. However, there are still lots of unsolved questions especially regarding what to use as base stations and what are corresponding results under the intrinsic complexity of alignment and configuration of components used for the RFID positioning. This study proposes the street light fixtures as base stations where the RFID receivers will be embedded for the mobile tracking scheme. As street light fixtures are usually installed at a certain distance interval, they can be used as base stations for the RFID receiver installation. Using the principle of the single row triangle network, the RFID receiver organization is determined based on the experiments such as recognition distance measurement and tag position accuracy estimation at inside and outside of the single row triangle network. The results verify that the mobile tracking scheme which uses RFID-embedded street light fixtures, suggested and configured in this study, is effective for the real-time outdoor positioning.

Object Tracking in Wireless Sensor Networks: Challenges and Solutions

Wireless Sensor Networks (WSNs) are small, inexpensive and battery-operated sensor nodes that are deployed over a geographical area. WSNs are used in many applications such border patrolling, military intrusion detection, wildlife animal monitoring, surveillance of natural disasters and healthcare systems. Mobile object tracking is a vital task in all these applications. The goal of this work is to highlight the most important challenges in the field of object tracking and provide a survey of the WSN architectural design and implementation approaches for tackling this problem. To that end, we analyze how each approach responds to each challenge and where it falls short. This analysis should provide researchers with a state-of-the-art review and inspire them to propose novel solutions.

Prediction-Based Object Tracking in Visual Sensor Networks

This paper is to investigate the mobile object tracking in visual sensor networks. When visual sensors equipped with cameras are randomly deployed in a monitoring environment, many sensors are involved in covering the same mobile object. In a visual sensor network, images of the object may be captured by different sensors in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving full coverage for a set of selected characteristic points of an object invariably involves a great deal of redundant image data consuming the transmission energy for a visual sensor network. A novel approach is proposed to overcome this problem. The minimal number of sensors required for set coverage can be determined by predicting the direction and speed of the mobile object. Such sets are capable of covering the maximal number of characteristic points of view related to the mobile object at one time. The simulation results show that this approach reduces transmission cost while preserving the maximal coverage range of mobile objects.

Near-Optimal Location Tracking Using Sensor Networks

We consider the problem of tracking mobile objects using a sensor network. We present a distributed tracking algorithm, called Mobile Object Tracking using Sensors (MOT), that scales well with the number of sensors and also with the number of mobile objects. MOT maintains a hierarchical structure of detection lists of objects that can efficiently track mobile objects and resolve object queries at any time. MOT guarantees that the cost to update (or maintain) its data structures will be at most O (min { log n, log D } ) times the optimal update cost and query cost will be within O (1) of the optimal query cost in the constant-doubling graph model, where n and D , respectively, are the number of nodes and the diameter of the network. MOT achieves polylogarithmic approximations for both costs in the general graph model. MOT balances the object and bookkeeping information load at each node in the expense of only O (log n ) increase in the update and query costs. The experimentation evaluation in both one by one and concurrent execution situations shows that MOT performs well in practical scenarios. To the best of our knowledge, MOT is the first algorithm for this problem in a distributed setting that is traffic-oblivious, i.e. agnostic to a priori knowledge of objects movement patterns, mobility and query rate, etc., and is load balanced. All previous solutions for this problem assumed traffic-consciousness in constructing the tracking data structure.Â