Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare

This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. First, a compressed-domain object segmentation scheme is performed to extract moving objects based on global motion estimation and local motion clustering. After detecting the moving objects, three compressed-domain features of each object are then extracted for identifying and locating fall incidents. The proposed system can differentiate fall-down from squatting by taking into account the event duration. Our experiments show that the proposed method can correctly detect fall incidents in real time.

[1]  Boon-Lock Yeo.  Efficient processing of compressed images and video , 1996 .

[2]  Vincent Mor,et al.  Multiple Stumbles: A Risk Factor for Falls in Community‐Dwelling Elderly; A Prospective Study , 1990, Journal of the American Geriatrics Society.

[3]  G. Williams,et al.  A smart fall and activity monitor for telecare applications , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[4]  Ming-Ting Sun,et al.  Global motion estimation from coarsely sampled motion vector field and the applications , 2003, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Gian Luca Foresti,et al.  Special issue on video communications, processing, and understanding for third generation surveillance systems , 2001 .

[6]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, ICPR 2004.

[7]  David S. Doermann,et al.  Event detection from MPEG video in the compressed domain , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[8]  N. Noury.  A smart sensor for the remote follow up of activity and fall detection of the elderly , 2002, 2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.02EX578).

[9]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[11]  Toshiyo Tamura,et al.  An ambulatory fall monitor for the elderly , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[12]  Chia-Wen Lin,et al.  Compressed-domain fall incident detection for intelligent home surveillance , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[13]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).