448 research outputs found
Evidential combination of pedestrian detectors
International audienceThe importance of pedestrian detection in many applications has led to the development of many algorithms. In this paper, we address the problem of combining the outputs of several detectors. A pre-trained pedestrian detector is seen as a black box returning a set of bounding boxes with associated scores. A calibration step is first conducted to transform those scores into a probability measure. The bounding boxes are then grouped into clusters and their scores are combined. Different combination strategies using the theory of belief functions are proposed and compared to probabilistic ones. A combination rule based on triangular norms is used to deal with dependencies among detectors. More than 30 state-of-the-art detectors were combined and tested on the Caltech Pedestrian Detection Benchmark. The best combination strategy outperforms the currently best performing detector by 9% in terms of log-average miss rate
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
International audienceThe 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
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Uncertainty estimation is crucial in safety-critical settings such as
automated driving as it provides valuable information for several downstream
tasks including high-level decision making and path planning. In this work, we
propose EvCenterNet, a novel uncertainty-aware 2D object detection framework
using evidential learning to directly estimate both classification and
regression uncertainties. To employ evidential learning for object detection,
we devise a combination of evidential and focal loss functions for the sparse
heatmap inputs. We introduce class-balanced weighting for regression and
heatmap prediction to tackle the class imbalance encountered by evidential
learning. Moreover, we propose a learning scheme to actively utilize the
predicted heatmap uncertainties to improve the detection performance by
focusing on the most uncertain points. We train our model on the KITTI dataset
and evaluate it on challenging out-of-distribution datasets including BDD100K
and nuImages. Our experiments demonstrate that our approach improves the
precision and minimizes the execution time loss in relation to the base model
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
A detailed environment perception is a crucial component of automated
vehicles. However, to deal with the amount of perceived information, we also
require segmentation strategies. Based on a grid map environment
representation, well-suited for sensor fusion, free-space estimation and
machine learning, we detect and classify objects using deep convolutional
neural networks. As input for our networks we use a multi-layer grid map
efficiently encoding 3D range sensor information. The inference output consists
of a list of rotated bounding boxes with associated semantic classes. We
conduct extensive ablation studies, highlight important design considerations
when using grid maps and evaluate our models on the KITTI Bird's Eye View
benchmark. Qualitative and quantitative benchmark results show that we achieve
robust detection and state of the art accuracy solely using top-view grid maps
from range sensor data.Comment: 6 pages, 4 tables, 4 figure
Traffic Scene Perception for Automated Driving with Top-View Grid Maps
Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen.
Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung.
Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden.
Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps.
Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum.
Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt.
Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht.
Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können.
Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving
Autonomous driving confronts great challenges in complex traffic scenarios,
where the risk of Safety of the Intended Functionality (SOTIF) can be triggered
by the dynamic operational environment and system insufficiencies. The SOTIF
risk is reflected not only intuitively in the collision risk with objects
outside the autonomous vehicles (AVs), but also inherently in the performance
limitation risk of the implemented algorithms themselves. How to minimize the
SOTIF risk for autonomous driving is currently a critical, difficult, and
unresolved issue. Therefore, this paper proposes the "Self-Surveillance and
Self-Adaption System" as a systematic approach to online minimize the SOTIF
risk, which aims to provide a systematic solution for monitoring,
quantification, and mitigation of inherent and external risks. The core of this
system is the risk monitoring of the implemented artificial intelligence
algorithms within the AV. As a demonstration of the Self-Surveillance and
Self-Adaption System, the risk monitoring of the perception algorithm, i.e.,
YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and
external collision risk are jointly quantified via SOTIF entropy, which is then
propagated downstream to the decision-making module and mitigated. Finally,
several challenging scenarios are demonstrated, and the Hardware-in-the-Loop
experiments are conducted to verify the efficiency and effectiveness of the
system. The results demonstrate that the Self-Surveillance and Self-Adaption
System enables dependable online monitoring, quantification, and mitigation of
SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT
Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework
International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented
TFDet: Target-aware Fusion for RGB-T Pedestrian Detection
Pedestrian detection plays a critical role in computer vision as it
contributes to ensuring traffic safety. Existing methods that rely solely on
RGB images suffer from performance degradation under low-light conditions due
to the lack of useful information. To address this issue, recent multispectral
detection approaches have combined thermal images to provide complementary
information and have obtained enhanced performances. Nevertheless, few
approaches focus on the negative effects of false positives caused by noisy
fused feature maps. Different from them, we comprehensively analyze the impacts
of false positives on the detection performance and find that enhancing feature
contrast can significantly reduce these false positives. In this paper, we
propose a novel target-aware fusion strategy for multispectral pedestrian
detection, named TFDet. Our fusion strategy highlights the pedestrian-related
features while suppressing unrelated ones, resulting in more discriminative
fused features. TFDet achieves state-of-the-art performance on both KAIST and
LLVIP benchmarks, with an efficiency comparable to the previous
state-of-the-art counterpart. Importantly, TFDet performs remarkably well even
under low-light conditions, which is a significant advancement for ensuring
road safety. The code will be made publicly available at
\url{https://github.com/XueZ-phd/TFDet.git}
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