Belief Function Definition for Ensemble Methods - Application to Pedestrian Detection in Dense Crowds

Vandoni, Jennifer; Hegarar-Mascle, Sylvie Le; Aldea, Emanuel

doi:10.23919/icif.2018.8455313

Cited by 2 publications

(4 citation statements)

References 25 publications

(40 reference statements)

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“…In the following, notations are slightly modified from [39] to let appear the dependency from both the considered pixel or sample (noted x) and the considered classifier (noted i). More in detail, dilation δ w and erosion E w operators depending on the struc-300 turing element of width w are composed with the calibrated sigmoid σ i relative to classifier i, in order to derive the two different sigmoid functions, denoted (δ w • σ i )…”

Section: Bba Definition Based On Pixel Neighborhood Information 275mentioning

confidence: 99%

“…In the context of high-density crowd pedestrian detection, in [39] we proposed a robust fusion strategy based on the Belief Functions (BF) framework [40,41,42]. The 110 evidential framework [43,44,36,45] is indeed able to naturally model the concept of imprecision, that in our case can arise in two different and complementary ways: in the derivation of posterior probability values from SVM decision scores, and later, from the spatial layout of the detections in the output image space.…”

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confidence: 99%

“…In particular, the bba allocation proposed in[39] is able to take into account both types of imprecision, aiming to be more robust to possible imperfections of the learned sigmoid from which the mapping from SVM scores to probability values is made, while at the same time taking into account the information coming from neighboring pixels in the image space. Practically, we process two successive discounting steps on 290 the initial Bayesian bba derived from the learned sigmoid.…”

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confidence: 99%

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Evidential query-by-committee active learning for pedestrian detection in high-density crowds

Vandoni

Aldea

Hégarat‐Mascle

2019

International Journal of Approximate Reasoning

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Mascle. Evidential query-by-committee active learning for pedestrian detection in high-density crowds. International Journal of Approximate Reasoning, Elsevier, In press, AbstractThe automatic detection of pedestrians in dense crowds has become recently a very active topic of research due to the implications for public safety, and also due to the increased frequency of large scale social events. The detection task is complicated by multiple factors such as strong occlusions, high homogeneity, small target size, etc., and different types of detectors are able to provide complementary interpretations of the input data, with varying individual levels of performance. Our first contribution consists in outlining a fusion strategy under the form of an ensemble method, which models the imprecision arising from each of the detectors, both in the calibration and in the spatial domains in an evidential framework. Then, we propose a sample selection for augmenting the training set used jointly by the committee of classifiers, based on evidential disagreement measures among the base members in a Query-by-Committee context. The results show that the proposed fusion algorithm is effective in exploiting the strengths of the individual classifiers, as well as in augmenting the training set with informative samples which allow the resulting detector to enhance its performance.

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Section: Bba Definition Based On Pixel Neighborhood Information 275mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Evidential query-by-committee active learning for pedestrian detection in high-density crowds

Vandoni

Aldea

Hégarat‐Mascle

2019

International Journal of Approximate Reasoning

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“…As in [11,55,56], a spatial Gaussian structuring element fitted in a window of radius a is used, to better take into account the spatial consistency.…”

Section: Evidential Multiple Classifier Systemmentioning

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Augmenting Deep Learning Performance in an Evidential Multiple Classifier System

Vandoni

Hégarat‐Mascle

Aldea

2019

Sensors

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The main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier’s respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision.

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Belief Function Definition for Ensemble Methods - Application to Pedestrian Detection in Dense Crowds

Cited by 2 publications

References 25 publications

Evidential query-by-committee active learning for pedestrian detection in high-density crowds

Evidential query-by-committee active learning for pedestrian detection in high-density crowds

Augmenting Deep Learning Performance in an Evidential Multiple Classifier System

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