Locating moving objects in car-driving sequences

Abstract

This paper presents a system for the search and detection of moving objects in a sequence of images previously captured by a camera installed in a conventional vehicle. The objective is the design and implementation of a software system based on optical flow analysis to detect and identify moving objects as perceived by a driver, taking into account that these objects could interfere with the driver's behavior, either through distraction or by posing an actual danger that may require an active response. The problem presents significant difficulties because the vehicle travels on conventional roads, open to normal traffic. Consequently, the scenes are recorded with natural lighting, i.e., under highly variable conditions (intensity, shadows, etc.). Furthermore, the use of a moving camera makes it difficult to properly identify static objects such as the road itself, signals, buildings, landscapes, and moving objects of the same speed, such as pedestrians or other vehicles. The proposed method consists of three stages. First, the optical flow is calculated for each image of the sequence, as a first estimate of the apparent motion. In a second step, two segmentation processes are addressed: the optical flow itself and the images of the sequence. Finally, in the last stage, the results of these two segmentation processes are combined to obtain the movement of the objects present in the sequence, identifying both their direction and magnitude. The quality of the results obtained with different sequences of real images makes this software suitable for systems to study driver behavior and to help detect danger situations, as various international traffic agencies consider in their research projects.

Spatial Multilevel Optical Flow Architecture-based Dynamic Motion Estimation in Vehicular Traffic Scenarios

“…For instance, in [17], one of the pioneers approaches known as Lukas-Kanade studies the cost effectiveness of the image registration techniques used for motion analysis and proposes to use the spatial intensity information to estimate the pixel that generates the best matching score in the images. Consequently, some algorithms also try to overcome those facts by the inclusion of hardware [18] or by the estimation based on the theory of warping [19], a pyramidal implementation of images [20], or by smoothing using robust kernels [21].…”

Design and fabrication of a large area freestanding compressive stress SiO2optical window

“…Image-sensing applications are increasingly important nowadays [1][2][3][4]. Support for safety and assurance is one such example.…”

“…Support for safety and assurance is one such example. The detection of moving objects [1] and pedestrians [2] has been investigated. Image-sensing applications are also expanding their scope to the fields of security, medicine, comp uter graphics, etc.…”