TY - GEN
T1 - Mining surveillance video for independent motion detection
AU - Zhang, Zhongfei
PY - 2002
Y1 - 2002
N2 - This paper addresses the special applications of data mining techniques in homeland defense. The problem targeted, which is frequently encountered in military/intelligence surveillance, is to mine a massive surveillance video database automatically collected to retrieve the shots containing independently moving targets. A novel solution to this problem is presented in this paper, which offers a completely qualitative approach to solving for the automatic independent motion detection problem directly from the compressed surveillance video in a faster than real-time mining performance. This approach is based on the linear system consistency analysis, and consequently is called QLS. Since the QLS approach only focuses on what exactly is necessary to compute a solution, it saves the computation to a minimum and achieves the efficacy to the maximum. Evaluations from real data show that QLS delivers effective mining performance at the achieved efficiency.
AB - This paper addresses the special applications of data mining techniques in homeland defense. The problem targeted, which is frequently encountered in military/intelligence surveillance, is to mine a massive surveillance video database automatically collected to retrieve the shots containing independently moving targets. A novel solution to this problem is presented in this paper, which offers a completely qualitative approach to solving for the automatic independent motion detection problem directly from the compressed surveillance video in a faster than real-time mining performance. This approach is based on the linear system consistency analysis, and consequently is called QLS. Since the QLS approach only focuses on what exactly is necessary to compute a solution, it saves the computation to a minimum and achieves the efficacy to the maximum. Evaluations from real data show that QLS delivers effective mining performance at the achieved efficiency.
UR - https://www.scopus.com/pages/publications/33749052379
U2 - 10.1109/ICDM.2002.1184043
DO - 10.1109/ICDM.2002.1184043
M3 - Conference contribution
SN - 0769517544
SN - 9780769517544
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 741
EP - 744
BT - Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
PB - Institute of Electrical and Electronics Engineers Inc.
ER -