An Indoor Video Surveillance System with Intelligent Fall Detection Capability

This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN) algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.