A comparative study of applying low-latency smoothing filters in a multi-kinect virtual play environment
Document Type
Conference Proceeding
Publication Date
1-1-2016
Abstract
The Skeleton Tracking System in Kinect is known for being noisy and unstable, hence, in practice, a noise reduction filter or smoothing filter needs to be employed before consuming the data in order to obtain smooth joint position data over time. In this paper, we present a comparative study on applying four different smoothing filters (Simple Moving Average Smoothing, Savitzky–Golay filter, Exponential filter, and Double Exponential filter) in “Alone Together” (Tang et al. 2015), a virtual play environment augmented with multiple sets of Kinects. Overall, among the four filters, the Exponential Smoothing Filter yields the best results in the game. The comparative study only provides quantitative observations on the four smoothing filters, the qualitative examination in terms of player satisfaction remains unclear, which is one of our immediate future research paths in this direction.
Publication Title
Communications in Computer and Information Science
First Page Number
144
Last Page Number
148
DOI
10.1007/978-3-319-40542-1_23
Recommended Citation
Tang, Tiffany Y. and Wang, Relic Yongfu, "A comparative study of applying low-latency smoothing filters in a multi-kinect virtual play environment" (2016). Kean Publications. 1813.
https://digitalcommons.kean.edu/keanpublications/1813