Scale-Invariant Local Descriptor for Event Recognition in 1D Sensor Signals

05/28/2011
by   Jierui Xie, et al.
0

In this paper, we introduce a shape-based, time-scale invariant feature descriptor for 1-D sensor signals. The time-scale invariance of the feature allows us to use feature from one training event to describe events of the same semantic class which may take place over varying time scales such as walking slow and walking fast. Therefore it requires less training set. The descriptor takes advantage of the invariant location detection in the scale space theory and employs a high level shape encoding scheme to capture invariant local features of events. Based on this descriptor, a scale-invariant classifier with "R" metric (SIC-R) is designed to recognize multi-scale events of human activities. The R metric combines the number of matches of keypoint in scale space with the Dynamic Time Warping score. SICR is tested on various types of 1-D sensors data from passive infrared, accelerometer and seismic sensors with more than 90

READ FULL TEXT
research
11/26/2014

Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition

Shapes and texture image recognition usage is an essential branch of pat...
research
12/29/2011

Descriptor learning for omnidirectional image matching

Feature matching in omnidirectional vision systems is a challenging prob...
research
02/09/2018

Shapes Characterization on Address Event Representation Using Histograms of Oriented Events and an Extended LBP Approach

Address Event Representation is a thriving technology that could change ...
research
03/30/2019

Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network

This paper presents a novel approach to exploit the distinctive invarian...
research
08/16/2022

An optimal sensors-based simulation method for spatiotemporal event detection

Human movements in urban areas are essential for understanding the human...
research
01/16/2018

Deep Multi-Spectral Registration Using Invariant Descriptor Learning

In this paper, we introduce a novel deep-learning method to align cross-...
research
01/22/2015

Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition

Motion trajectory recognition is important for characterizing the moving...

Please sign up or login with your details

Forgot password? Click here to reset