Moving object detection via TV-L1 optical flow in fall-down videos

Abstract

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.

Symmetrically Stacked Long Short-Term Memory Networks for Fall Event Recognition Using Compact Convolutional Neural Networks-Based Tracker

“…The goal of this subsection is to make a final decision on locating the exact fall frame using previous fall event classification features. Let i represents the current frame, ∆y best ∈ R 1 is calculated based on Equation (8), where y i is the current and y i−1 is the previous output scores in a video sequence of V length. ∆y i is measured since the output scores of fall event classification is in d classes, in which the fall frame is detected when the change in output scores is the biggest.…”

“…Lastly, the recovery phase is the condition when the person gets up again after the post-fall phase, where the timing is regarded as the response time, T 2 . In general, the main cause for the occurrence of a fall event can be attributed to a loss of balance due to sudden trip or slip, or instability during movements [7,8]. Furthermore, WHO has also reported that the fall event is the second biggest death contributor globally with an estimate of 646,000 cases each year.…”

Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation

“…1 shows the spatial distribution of the solution set of the optical flow equation. For those images where there are more prominent points, more obvious edge lines, and other information, median filtering is not good, because the filtered image will lose a lot of detailed information [20][21][22][23][24].…”