Overtaking Vehicle Detection Method and Its Implementation Using IMAPCAR Highly Parallel Image Processor

Abstract

SUMMARY This paper describes the real-time implementation of a vision-based overtaking vehicle detection method for driver assistance systems using IMAPCAR, a highly parallel SIMD linear array processor. The implemented overtaking vehicle detection method is based on optical flows detected by block matching using SAD and detection of the flows' vanishing point. The implementation is done efficiently by taking advantage of the parallel SIMD architecture of IMAPCAR. As a result, video-rate (33 frames/s) implementation could be achieved.

Parallelization of the optical flow computation in sequences from moving cameras

“…http://jivp.eurasipjournals.com/content/2014/1 /18 There have been many alternatives and they have evolved along with the technology. In some cases, SIMD processor arrays with specific chips, either existing [36] or designed ad hoc for the computation of optical flow [13,17,[37][38][39], have been used. General-purpose MIMD as the connection machine [40,41], networks of transputers [42], or cellular neural networks [43,44] were also used in the past.…”

“…A system for driving assistance is presented in [17]. It detects vehicles approaching from behind and alerts the driver when performing lane change maneuvers.…”

“…In any case, the problem is computationally very complex, so most of the proposed solutions are based on strong simplifications adapted to the technology available at the time or to the specific applications they intend to solve. In some cases, the size of the images is so small that can hardly represent a real-life scene [9][10][11][12]. In other cases, determining parameters in the generation of optical flow such as the light source (source, intensity, variation, etc.)…”

“…or the motion of objects within the scene are restricted [13][14][15]. Except for some recent articles normally associated with the movement of conventional vehicles [16,17], mobile robots [18,19], and handheld cameras [20,21], the majority of papers describe systems in which the variation of the scene is limited. This limitation is determined because they work with images with a static background and taken with a static camera.…”

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

“…Likewise, the relation between v 2 and H 1 can be obtained by (3), as shown as (5). Further by applying (5), we can have the height of the cuboid C as in (6).…”