One of the more startling eflects of road related accidents is the economic and social burden they muse. Between 750,000 and 880,000 people died globally in mad related accidents in 1999 alone, with an estimated cost of US518 billion 1111. One way of combating this problem is to develop Intelligent Vehicles that are selfaware and act to inmase the safety of the tmnsportation system. This paper presents the development and applicotion of a novel multiple-cue visual lane tmcking system for research into Intelligent Vehicles (IV). Particle jiltering and cue fusion technologies J o n the basis ofthe lane tmcking system which robustly handles seueml of the problems faced by previous lane tmcking system such (IS shadows on the mad, unreliable lane markings. dmmatic lighting changes and discontinuous changes in mad chamcteristics and types. Ezperimental results of the lane tracking system running at 15Hz will be discusaed, focusing on the particle flter and cue fusion technology used.
The goal of motion segmentation and layer extraction can be viewed as the detection and localization of occluding surfaces. A feature that has been shown to be a particularly strong indicator of occlusion, in both computer vision and neuroscience, is the T-junction; however, little progress has been made in T-junction detection. One reason for this is the difficulty in distinguishing false T-junctions (i.e. those not on an occluding edge) and real T-junctions in cluttered images. In addition to this, their photometric profile alone is not enough for reliable detection.This paper overcomes the first problem by searching for T-junctions not in space, but in space-time. This removes many false T-junctions and creates a simpler image structure to explore. The second problem is mitigated by learning the appearance of T-junctions in these spatiotemporal images.An RVM T-junction classifier is learnt from handlabelled data using SIFT to capture its redundancy. This detector is then demonstrated in a novel occlusion detector that fuses Canny edges and T-junctions in the spatiotemporal domain to detect occluding edges in the spatial domain.
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