Identification of Discards in Video Surveillance

DOI

10.2991/jiaet-18.2018.60How to use a DOI?

Keywords

Bayesian reasoning; Reversible jumping Markov chain Monte Carlo; Multi-target tracking; Posterior probability

Abstract

In this paper, a detection scheme combining Bayesian network modeling and deep neural network feature extraction is used to detect the remaining cabinets in monitoring. The iterative sampling method of RJMCMC is used to track the follow-up multi-targets, and the identification of the legacy box is realized by means of three behavioral modes and tracking states of the RJMCMC process. Experimental results show the effectiveness of the algorithm.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xiaohu Liu
AU  - Guojian Cheng
PY  - 2018/03
DA  - 2018/03
TI  - Identification of Discards in Video Surveillance
BT  - Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)
PB  - Atlantis Press
SP  - 338
EP  - 344
SN  - 2352-5401
UR  - https://doi.org/10.2991/jiaet-18.2018.60
DO  - 10.2991/jiaet-18.2018.60
ID  - Liu2018/03
ER  -