Tracking human movement in office environment using video processing

Ramli, M. S. Azwan; Zamzuri, Hairi; Abidin, Mastura Shafinaz Zainal

doi:10.1109/icmsao.2011.5775519

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PF became widely used in tracking after being proposed by Isard and Blake [5]. Much success has been achieved in visual tracking [5], [6], [7] and audio tracking [8], [9]. It has been shown in many studies [10], [11], [12], [13] using AV data in tracking gives more reliable results than using each individually.…”

Section: Related Workmentioning

confidence: 99%

Audio constrained particle filter based visual tracking

Kılıç

Barnard

Wang

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

We present a robust and efficient audio-visual (AV) approach to speaker tracking in a room environment. A challenging problem with visual tracking is to deal with occlusions (caused by the limited field of view of cameras or by other speakers). Another challenge is associated with the particle filtering (PF) algorithm, commonly used for visual tracking, which requires a large number of particles to ensure the distribution is well modelled. In this paper, we propose a new method of fusing audio into the PF based visual tracking. We use the direction of arrival angles (DOAs) of the audio sources to reshape the typical Gaussian noise distribution of particles in the propagation step and to weight the observation model in the measurement step. Experiments on AV16.3 datasets show the advantage of our proposed method over the baseline PF method for tracking occluded speakers with a significantly reduced number of particles. © 2013 IEEE

show abstract

Section: Related Workmentioning

confidence: 99%

Audio constrained particle filter based visual tracking

Kılıç

Barnard

Wang

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

show abstract

“…Despite its high accuracy, the IMU revealed discomfort in daily use because of the amount of IMUs that need be attached to a body and the complex setup procedure. Video capture using cameras has proven to be relatively accurate as well [ 8 , 9 , 10 , 11 ]. However, camera-based motion sensing technology is not feasible when a direct line of sight obstructed.…”

Section: Introductionmentioning

confidence: 99%

Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning

Anderson

Choffin

Jeong

et al. 2022

Sensors

View full text Add to dashboard Cite

This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.

show abstract