Coupled detection, association and tracking for Traffic Sign Recognition

Boumediene, Mohammed; Lauffenburger, Jean-Philippe; Daniel, Jérémie; Cudel, Christophe

doi:10.1109/ivs.2014.6856492

Cited by 11 publications

(5 citation statements)

References 25 publications

(39 reference statements)

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“…A corner detector is associated to insure detection efficiency. Thanks to this solution to feedback loop between tracking algorithms an ROI detector [9] III.…”

Section: Literature Surveymentioning

confidence: 99%

Image Processing Approach to Identify and Recognize Traffic Symbols for Color Images Using Fuzzy Integral

2016

IJAERD

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“…A corner detector is associated to insure detection efficiency. Thanks to this solution to feedback loop between tracking algorithms an ROI detector [9] III.…”

Section: Literature Surveymentioning

confidence: 99%

Image Processing Approach to Identify and Recognize Traffic Symbols for Color Images Using Fuzzy Integral

2016

IJAERD

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“…The LISA-TS Extension is split into a training set with every 5th frame annotated and a test set with every frame annotated, so it is also suitable for testing traffic sign tracking systems. Tracking is outside of the scope of this paper, but see [10] for a simple tracking experiment and [9] for a more advanced solution.…”

Section: A Datasetmentioning

confidence: 99%

“…For temporal grouping, tracking comes into play. There has been some research into tracking of traffic signs [9], [10], but it is still in its infancy. One of the issues in traffic sign tracking is that no suitable data set exists for that purpose, something the data set extension put forth in this paper addresses, even though we do not tackle that issue in the experiments here.…”

Section: Introductionmentioning

confidence: 99%

Detection of U.S. Traffic Signs

Møgelmose

Liu

Trivedi

2015

IEEE Trans. Intell. Transport. Syst.

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This paper presents a comprehensive research study of the detection of U.S. traffic signs. Until now, the research in Traffic Sign Recognition systems has been centered on European traffic signs, but signs can look very different across different parts of the world, and a system that works well in Europe may indeed not work in the U.S. We go over the recent advances in traffic sign detection and discuss the differences in signs across the world. Then we present a comprehensive extension to the publicly available LISA-TS traffic sign data set, almost doubling its size, now with high-definition-quality footage. The extension is made with testing of tracking sign detection systems in mind, providing videos of traffic sign passes. We apply the Integral Channel Features and Aggregate Channel Features detection methods to U.S. traffic signs and show performance numbers outperforming all previous research on U.S. signs (while also performing similarly to the state of the art on European signs). Integral Channel Features have previously been used successfully for European signs, whereas Aggregate Channel Features have never been applied to the field of traffic signs. We take a look at the performance differences between the two methods and analyze how they perform on very distinctive signs, as well as white, rectangular signs, which tend to blend into their environment.

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“…This approach has been extended by Mercier et al [20] to track vehicles by using a global optimization to make assignment decisions. To reduce the complexity for real-time applications, a local optimization has been used [5,6]. For all these methods, the data fusion process begins by defining belief masses from sensor information and prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…For data association applications, the widely used probabilistic transformation (i.e. approximation) is the pignistic transformation [5,6,17,20]. This transformation is based on a simple mapping process from belief to probability domain.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Probabilistic Transformations for Evidential Data Association

Boumediene

Dezert

2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Data association is one of the main tasks to achieve in perception applications. Its aim is to match the sensor detections to the known objects. To treat such issue, recent research focus on the evidential approach using belief functions, which are interpreted as an extension of the probabilistic model for reasoning about uncertainty. The data fusion process begins by quantifying sensor data by belief masses. Thereafter, these masses are combined in order to provide more accurate information. Finally, a probabilistic approximation of these combined masses is done to make-decision on associations. Several probabilistic transformations have been proposed in the literature. However, to the best of our knowledge, these transformations have been evaluated only on simulated examples. For this reason, the objective of this paper is to benchmark most of interesting probabilistic transformations on real-data in order to evaluate their performances for the autonomous vehicle perception problematic.

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Coupled detection, association and tracking for Traffic Sign Recognition

Cited by 11 publications

References 25 publications

Image Processing Approach to Identify and Recognize Traffic Symbols for Color Images Using Fuzzy Integral

Image Processing Approach to Identify and Recognize Traffic Symbols for Color Images Using Fuzzy Integral

Detection of U.S. Traffic Signs

Evaluation of Probabilistic Transformations for Evidential Data Association

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