Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work [1],...
We present a system for recognising human behaviour given a symbolic representation of surveillance videos. The input of our system is a set of timestamped short-term behaviours, t...
Among the various types of semantic concepts modeled, events pose the greatest challenge in terms of computational power needed to represent the event and accuracy that can be ach...
We propose a space-time Markov Random Field (MRF)
model to detect abnormal activities in video. The nodes in
the MRF graph correspond to a grid of local regions in the
video fra...
Jaechul Kim (University of Texas at Austin), Krist...
Acoustic event detection (AED) aims to identify both timestamps and types of multiple events and has been found to be very challenging. The cues for these events often times exist...
Po-Sen Huang, Xiaodan Zhuang, Mark Hasegawa-Johnso...