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A single feature extractor-classifier is not usually able to deal with the diversity of multiple image scenarios. Therefore, integration of features and classifiers can bring benefits to cope with this problem, particularly when the parts are carefully chosen and synergistically combined. In this paper, we address the problem of pedestrian detection by a novel ensemble method. Initially, histograms...
This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the...
Reliable detection and classification of vulnerable road users constitute a critical issue on safety/protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems have LIDAR and/or radar as primary detection modules and vision-based systems for object classification. This work, on the other hand, presents a valuable analysis of pedestrian detection...
Some researches have demonstrated that a single recognition system is not usually able to deal with the diversity of environment situations in images. In this paper, with the aim of finding a robust method to compensate single classifier inability under certain circumstances, an extensive study on how to combine features and classifiers is performed. Two ways of integrating features and classifiers...
Humans refer almost to everything by their characterization rather than their detailed descriptions. For example, in indoor environments places are specified as: rooms, corridors, etc. Such categorizations, if learned by a robot, could improve the capabilities in the areas of navigation, localization, or human- robot cooperation. This paper studies the problem of categorizing environments into semantic...
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