Bayesian Estimation-Based Pedestrian Tracking in Microcells

We consider a pedestrian tracking system where sensor nodes are placed only at specific points so that the monitoring region is divided into multiple smaller regions referred to as microcells. In the proposed pedestrian tracking system, sensor nodes composed of pairs of binary sensors can detect pedestrian arrival and departure events. In this paper, we focus on pedestrian tracking in microcells. First, we investigate actual pedestrian trajectories in a microcell on the basis of observations using video sequences, after which we prepare a pedestrian mobility model. Next, we propose a method for pedestrian tracking in microcells based on the developed pedestrian mobility model. In the proposed method, we extend the Bayesian estimation to account for time-series information to estimate the correspondence between pedestrian arrival and departure events. Through simulations, we show that the tracking success ratio of the proposed method is increased by 35.8% compared to a combinatorial optimization-based tracking method.

Download Full-text

Wearable energy-efficient fitness tracking system for sports person health monitoring application

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219158 ◽

2021 ◽

pp. 1-13

Author(s):

Dan Xie ◽

Ming Zhang ◽

Priyan Malarvizhi Kumar ◽

Bala Anand Muthu

Keyword(s):

Health Monitoring ◽

Energy Efficient ◽

Tracking System ◽

Path Loss ◽

Integrated Approach ◽

Smartphone Application ◽

Sensor Nodes ◽

Engineering Research ◽

Fitness Tracking ◽

Monitoring Application

The high potential of wearable physiological sensors in regenerative medicine and continuous monitoring of human health is currently of great interest. In measuring in real-time and non-invasively highly heterogeneous constituents, have a great deal of work and therefore been pushed into creating several sports monitoring sensors. The advanced engineering research and technology lead to the design of a wearable energy-efficient fitness tracking (WE2FT) system for sports person health monitoring application. Instantaneous accelerations are measured against pulses, and specific walking motions can be tracked by this system using a deep learning-based integrated approach of an intelligent algorithm for gait phase detection for the proposed system (WE2FT). The algorithm’s effects are addressed, and the performance has been evaluated. In this study, the algorithm uses a smartphone application to track steps using the Internet of Things (IoT) technology. For this initiative, the central node’s optimal location is measured with the antenna reflectance coefficient and CM3A path loss model (IEEE 802.15.6) among the sensor nodes for energy-efficient communication. The simulation experiment results in the highest performance in terms of energy efficiency and path loss.

Download Full-text

A Modified Multi-Pedestrian Tracking System

2019 Chinese Control Conference (CCC) ◽

10.23919/chicc.2019.8865571 ◽

2019 ◽

Author(s):

Yuan Gong ◽

Jianning Chi ◽

Xiaosheng Yu ◽

Chengdong Wu ◽

Zixi Jia

Keyword(s):

Tracking System ◽

Pedestrian Tracking

Download Full-text

Bayesian estimation for the local assessment of the multifractality parameter of multivariate time series

2016 24th European Signal Processing Conference (EUSIPCO) ◽

10.1109/eusipco.2016.7760502 ◽

2016 ◽

Cited By ~ 2

Author(s):

S. Combrexelle ◽

H. Wendt ◽

Y. Altmann ◽

J.-Y. Tourneret ◽

S. McLaughlin ◽

...

Keyword(s):

Time Series ◽

Bayesian Estimation ◽

Multivariate Time Series ◽

Local Assessment

Download Full-text

Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks

Sensors ◽

10.3390/s18082568 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2568 ◽

Cited By ~ 2

Author(s):

Ruisong Wang ◽

Gongliang Liu ◽

Wenjing Kang ◽

Bo Li ◽

Ruofei Ma ◽

...

Keyword(s):

Sensor Networks ◽

Compressed Sensing ◽

Bayesian Estimation ◽

Sensor Node ◽

Optimization Problem ◽

Sensor Nodes ◽

Underwater Sensor Networks ◽

Node Selection ◽

Selection Scheme ◽

Error Covariance

Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.

Download Full-text

Surveillance and Tracking System with Collaboration of Robots, Sensor Nodes, and RFID Tags

10.1109/icccn.2009.5235295 ◽

2009 ◽

Cited By ~ 16

Author(s):

Yang Xiao ◽

Yanping Zhang

Keyword(s):

Tracking System ◽

Sensor Nodes ◽

Rfid Tags

Download Full-text

Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

Computational Intelligence and Neuroscience ◽

10.1155/2016/8163878 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Hui Li ◽

Yun Liu ◽

Chuanxu Wang ◽

Shujun Zhang ◽

Xuehong Cui

Keyword(s):

Particle Filters ◽

A Priori ◽

Texture Features ◽

Tracking Algorithm ◽

Video Sequences ◽

Particle State ◽

Pedestrian Tracking ◽

Estimation Problems ◽

Non Gaussian ◽

Gaussian Estimation

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

Download Full-text

Network-Based Pedestrian Tracking System with Densely Placed Wireless Access Points

Communications in Computer and Information Science - Information Search, Integration, and Personlization ◽

10.1007/978-3-319-68282-2_6 ◽

2017 ◽

pp. 82-96

Author(s):

Ryuta Abe ◽

Junpei Shimamura ◽

Kento Hayata ◽

Hiroaki Togashi ◽

Hiroshi Furukawa

Keyword(s):

Tracking System ◽

Wireless Access ◽

Pedestrian Tracking ◽

Access Points

Download Full-text

Research on Underwater Target Tracking Based on Contour Detection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.317-319.890 ◽

2011 ◽

Vol 317-319 ◽

pp. 890-896

Author(s):

Ming Jun Zhang ◽

Yuan Yuan Wan ◽

Zhen Zhong Chu

Keyword(s):

Prediction Model ◽

Target Tracking ◽

Tracking System ◽

Contour Detection ◽

Motion Prediction ◽

Moving Target ◽

Contour Extraction ◽

Tracking Accuracy ◽

Tracking Method ◽

Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

Download Full-text