RT Journal Article
A1 W. Bouachir
AD LICEF Res. Center, TELUQ, Montreal, QC
A1 R. Noumeir
AD Ecole de Technol. Supɛieure, Montreal, QC

PB iet
T1 Automated video surveillance for preventing suicide attempts
JN IET Conference Proceedings
SP 13 (6 .)
OP 13 (6 .)
AB Inmate suicide by hanging is documented as a major cause of death in prisons. Important efforts have been made to develop technological prevention tools, but the proposed solutions are mostly using cumbersome devices, in addition to their lack of generalizability. Nowadays, computer vision methods for real-time video analysis have experienced impressive progress. The recent emergence of RGB-D cameras clearly illustrates the achieved advances by offering new ways for machines to interpret human activity. There was however no significant works on exploiting this evolution, and as a result, CCTV systems used for monitoring suicidal inmates are still greatly depending on human attention and intervention. This paper proposes an intelligent video surveillance system for automated detection of suicide attempts by hanging. The proposed algorithm is able to efficiently model suicidal behavior by exploiting the depth information captured by an RGB-D camera. Activity detection is then performed by classifying visual features characterizing body joint movements. Our method demonstrated a high robustness on a challenging dataset including video sequences where suicide attempts are simulated.
K1 prison death
K1 suicidal behavior
K1 activity detection
K1 suicidal inmate monitoring
K1 RGB-D cameras
K1 automated video surveillance
K1 automated suicide attempt detection
K1 suicide attempt prevention
K1 real-time video analysis
K1 computer vision
K1 human attention
K1 body joint movements
K1 video sequences
K1 intelligent video surveillance system
K1 inmate suicide
K1 human intervention
K1 CCTV systems
K1 depth information
K1 visual feature classification
DO https://doi.org/10.1049/ic.2016.0081
UL https://digital-library.theiet.org/;jsessionid=pvvja3ja0enj.x-iet-live-01content/conferences/10.1049/ic.2016.0081
LA English
SN
YR 2016
OL EN