Video surveillance-based fall detection system using object-level feature thresholding and [formula omitted]numbers

Abstract

A new algorithm, namely Z−numbers-based deep feature thresholding (Z−DFT) is described for handling the issue concerning uncertainty that arises while classifying various fall and no-fall events in complex scenarios in both indoor (viz., home and hospital) and outdoor (viz., construction area and road). The Z−DFT consists of four phases: (i) object detection and tracking, (ii) feature extraction, (iii) feature thresholding and rule generation, and (iv) Z−numbers-based analysis for quantifying the reliability of the detected falls. Unlike state-of-the-art methods, Z−DFT uses both OpenPose and object-level features. This enables better modeling of both indoor and outdoor falls. New object-level features considered are change in area, aspect ratio, speed variation, and change in direction of the detected objects. The detection of fall locations involves two phases, viz., probable location and specific location. The probable location corresponding to each OpenPose and object-level feature is determined based on its statistical information. The commonality of these probable locations results in the specific location which is determined by framing linguistic rules using all the features. Z−numbers computed with features further reflect the reliability of detection. The characteristic features of Z−DFT are demonstrated over eighteen real-time videos acquired from YouTube8M and UR Fall data, along with its superiority claimed over ten state-of-the-art algorithms.