Search

In order for detection of a fire in fields, it is effective to detect smoke since it often behaves as a precursor of the fire. One preferable way for early detection is to use visual information from outdoor cameras that widely monitor the filed. There have been many attempts to detect smokes via optical sensors on digital cameras using optical flow methods, but not fully successful from practical-use aspects. It is because the area of smokes occupying on the footage by outdoor cameras is not necessarily large enough. Moreover, in case of urban cities, discrimination of the smokes from other moving objects such as cars, trees and turbines is not easy. Herein we propose a novel method to detect daytime smokes based on variance of optical flow and characteristics of HSV (hue-saturation-value) color. We apply the method to a set of footage of three days obtained in an industrial zone in Japan. Successful results are obtained as over 90% of smokes are detected. Notable is that this method is independent of solar radiation conditions on sunny and cloudy days. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to investigate whether an artificial optic flow created by dot motion guides attention in a driving scene.Background: To achieve safe driving, it is essential to understand the characteristics of human visual information processing as well as to provide appropriate support for drivers. Past research has demonstrated that expanding optic flow guides visual attention to the focus of expansion. Optic flow is an attractive candidate for use as a cue to direct drivers' attention toward the significant information. The question addressed concerns whether an artificial optic flow can successfully guide attention even in a traffic situation involving the optic flow that naturally occurs while driving.Method: We developed a visual search paradigm embedded in a video of a driving scene. Participants first observed an optic flow motion pattern superimposed on the video for brief period; next, when the optic flow and video ceased, they searched a static display for a target among multiple distractors.Results: The target detection was faster when a target's locus coincided with the implied focus of expansion from the preceding optic flow (vs. other loci).Conclusion: The artificial optic flow guides attention and facilitates searching objects at the focus of expansion even when the optic flow was superimposed on a driving scene.Application: Optic flow can be an effective cue for guiding drivers' attention in a traffic situation. This finding contributes to the understanding of visual attention in moving space and helps develop technology for traffic safety. [ABSTRACT FROM AUTHOR]

Group activity recognition of humans is an important task for video surveillance systems. In complex situations, however, ambiguities usually arise as a result of chaotic movements in the scene, especially when it is solved based upon motion trajectory analysis involving object segmentation and tracking. In this work, we present an interaction force model (IFM) to recognize group activity using low‐level feature based on dense optical flow. The IFM used to define interaction among people consists of three principal forces: group motion force (GMF), interaction motion force (IMF), and reciprocal motion force (RMF), to model the behaviors of the activity. Our algorithm is evaluated using the well‐known NUS‐HGA and BEHAVE datasets. Results of our approach reveal good efficiency with high accuracy, regardless of the surrounding conditions, and is competitive with other state‐of‐the‐art methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper presents a method in which moving obstacles such as pedestrians, bicycles, and automobiles are detected from a moving vehicle using images, and the locations of the obstacles are calculated stably in relation to the vehicle. In the proposed method, stereoptic images are projected on the estimated road plane, and a free-space map is constructed without attempting matching between the stereoptic images. Then, the vanishing point of the motion (FOE) is estimated from the optical flow, and the moving obstacle area is extracted from the image based on the residual error. By projecting the moving obstacle area onto the free-space map, the location of the moving obstacle relative to the moving vehicle is determined. In this method, neither tracking between frames nor matching search between stereoptic images is needed, and the location can be determined stably, even for objects with rapidly varying size in the image, or nonrigid objects with continuously changing shape. An experiment was performed in which the locations of moving obstacles such as pedestrians, bicycles, and automobiles were determined from the input image sequence obtained from a camera on a moving vehicle. The effectiveness of the proposed method was demonstrated. © 2001 Scripta Technica, Syst Comp Jpn, 32(14): 67–76, 2001 [ABSTRACT FROM AUTHOR]