An intelligent vision-based system for detecting self-harm behavior

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Self-harm is a common cause of death in correctional settings. The constant monitoring and the rapid detection**of self-harm events are crucial for protecting inmates' lives and reducing the mortality rates. During the last**decades, several technologies have been developed for preventing suicide attempts in prisons. However, most**of them use cumbersome devices and/or are greatly depending on human guard attention and intervention. Our**objective is to develop autonomous video surveillance solutions in order to intelligently detect life-threatening**events in prisons, and allow a rapid and reactive intervention. The proposed system will be designed to operate**continuously in realistic conditions to detect suicide attempts by hanging or by wrist cutting; 2- fights between**two inmates; 3- and symptoms of life threatening situations. These objectives will be achieved by ensuring the**efficient exploitation of recent advances in visual acquisition technologies, computer vision methods and**artificial intelligence. This project will lead to new knowledge in the intelligent video surveillance domain,**while contributing to training highly qualified personnel who will acquire an expertise in high demand across**Canada. Our work will also allow our industrial partner to develop a commercial product to be deployed in**correctional services. The impact of this product is expected to be more than merely economic. It will help**saving the lives of incarcerated people at risk, and avoid the resulting suffering.