Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection

Abnormal event detection has been an important issue in video surveillance applications. Due to the huge amount of surveillance data, only a small proportion could be loaded during the training. As a result, there is a high chance of incomplete normal patterns in the training data, which makes the task very challenging. Sparse representation, as one of solutions, has shown its effectiveness. Th...

Unsupervised Methods for Activity Analysis and Detection of Abnormal Events

Sensor Errors and the Uncertainties in Stereo Reconstruction

An important objective in the evaluation of algorithms with sensory inputs is the devel opment of measures characterizing the intrinsic errors in the results Intrinsic are those errors which are caused by noise in the input data The particular application which we consider is D reconstruction from stereo We demonstrate that a radiometric correction of the images could improve signi cantly the a...

Adaptive Signal Detection in Auto-Regressive Interference with Gaussian Spectrum

A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown...

Sparse Signal Reconstruction via Iterative Support Detection

We present a novel sparse signal reconstruction method “ISD”, aiming to achieve fast reconstruction and a reduced requirement on the number of measurements compared to the classical `1 minimization approach. ISD addresses failed reconstructions of `1 minimization due to insufficient measurements. It estimates a support set I from a current reconstruction and obtains a new reconstruction by solv...