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dissertacao_marcelo_mendonca.pdf: 29068279 bytes, checksum: 80fb8fb6ea4e3852373e2a42c4467ea6 (MD5) / Correctly identifying the road area on an image is a crucial task for many traffic analyses
based on surveillance cameras and computer vision. Despite that, most of the systems do
not provide this functionality in an automatic fashion; instead, the road area needs to be
annotated by tedious and inefficient manual processes. This situation results in further
inconveniences when one deals with a lot of cameras, demanding considerable effort to
setup the system. Besides, since traffic analysis is an outdoor activity, cameras are exposed
to disturbances due to natural events (e.g., wind, rain and bird strikes), which may require
recurrent system reconfiguration. Although there are some solutions intended to provide
automatic road detection, they are not capable of dealing with common situations in
urban context, such as poorly-structured roads or occlusions due to objects stopped in
the scene. Moreover in many cases they are restricted to straight-shaped roads (commonly
freeways or highways), so that automatic road detection cannot be provided in most of
the traffic scenarios.
In order to cope with this problem, we propose a new approach for road detection.
Our method is based on a set of innovative solutions, each of them intended to address
specific problems related to the detection task. In this sense, a context-aware background
modeling method has been developed, which extracts contextual information from the
scene in order to produce background models more robust to occlusions. From this point,
segmentation is performed to extract the shape of each object in the image; this is accomplished
by means of a superpixel method specially designed for road segmentation,
which allows for detection of roads with any shape. For each extracted segment we then
compute a set of features, the goal of which is supporting a decision tree-based classifier
in the task of assigning the objects as being road or non-road. The formulation of our
method — a road detection carried out by a combination of multiple features — makes it
able to deal with situations where the road is not easily distinguishable from other objects
in the image, as when the road is poorly-structured.
A thorough evaluation has indicated promising results in favour of this method. Quantitatively,
the results point to 75% of accuracy, 90% of precision and 82% of recall over
challenging traffic videos caught in non-controlled conditions. Qualitatively, resulting
images demonstrate the potential of the method to perform road detection in different
situations, in many cases obtaining quasi-perfect results.
| Identifer | oai:union.ndltd.org:IBICT/oai:192.168.11:11:ri/23039 |
| Date | 17 February 2014 |
| Creators | Santos, Marcelo Mendonça dos |
| Contributors | Oliveira, Luciano Rebouças de, Oliveira, Luciano Rebouças de, Ferreira Júnior, Perfilino Eugênio, Mirisola, Luiz Gustavo |
| Publisher | Escola Politécnica /Instituto de Matemática., Programa de Pós-Graduação em Mecatrônica, UFBA, brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFBA, instname:Universidade Federal da Bahia, instacron:UFBA |
| Rights | info:eu-repo/semantics/openAccess |
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