FALL DETECTION USING MODULAR NEURAL NETWORKS WITH BACK-PROJECTED OPTICAL FLOW

This paper presents a video-based algorithm for fall detection used the modular neural networks with the motion vectors computed by block-based optical flow back-projection (BOFB). From a video sequence, the moving object is extracted first and the pixels with high intensity variance in the extracted object are determined as feature points. The motion vector in this application is required to represent the actual motion displacement, rather than visually significant similarity. Therefore, we proposed BOFB which back-projects optical flows in a block to restore the motion vector from gradient-based optical flows that is employed to estimate the genuine motion of these feature points. The sequences of feature vectors are fed into the proposed Time-Delay Hierarchical modular Neural Network (TDHNN) for fall detection. The TDHNN consists of two major modular networks: several Time-Delay Neural Networks (TDNNs) trained by various feature characteristics and a Support Vector Machine (SVM) for final decision. This paper also purposed Slide Window Accumulate (SWA) mechanism for the increase of the robustness of the system in fall detection. The experimental results show that the proposed algorithm is efficacious and reliable in fall detection.