Proceedings ArticleDOI
Pedestrian Detection for Autonomous Driving within Cooperative Communication System
Amal Hbaieb,Jihene Rezgui,Lamia Chaari +2 more
- pp 1-6
TLDR
This paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system, using Histogram of Oriented Gradients descriptor with the linear Support Vector Machine classifier, and Haar feature-based cascade classifier to reach vehicle detection.Abstract:
The ability to perceive and understand surrounding road-users behaviors is crucial for self-driving vehicles to correctly plan reliable reactions. Computer vision that relies mostly on machine learning techniques enables autonomous vehicles to perform several required tasks such as pedestrian detection. Furthermore, within a fully autonomous driving environment, driverless vehicle has to communicate and share perceived data with its neighboring vehicles for more safe navigation. In this context, our paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system. To achieve this purpose, pedestrian and vehicle recognition is required. Thus, we implemented intended detectors. We used Histogram of Oriented Gradients (HOG) descriptor with the linear Support Vector Machine (SVM) classifier for the pedestrian detector, and Haar feature-based cascade classifier to reach vehicle detection. The performance evaluation of our solution leads to fairly good detection accuracy around 90% for pedestrian and 88% for vehicle.read more
Citations
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Posted Content
Generalizable Pedestrian Detection: The Elephant In The Room
TL;DR: It is found that existing state-of-the-art pedestrian detectors, though perform quite well when trained and tested on the same dataset, generalize poorly in cross dataset evaluation, and it is illustrated that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection.
Proceedings ArticleDOI
Generalizable Pedestrian Detection: The Elephant In The Room
TL;DR: In this article, the authors proposed a progressive training pipeline for autonomous-driving oriented pedestrian detection and found that a general purpose object detector, without pedestrian-tailored adaptation in design, generalizes much better compared to existing state-of-the-art pedestrian detectors.
Journal ArticleDOI
A Comprehensive Survey on Autonomous Driving Cars: A Perspective View
TL;DR: The survey of machine learning algorithms and techniques applied in the design of autonomous driving system over a decade in terms of prediction time and accuracy has been documented and compared.
Journal ArticleDOI
Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges
Sundas Iftikhar,Zuping Zhang,Muhammad Asim,Ammar Muthanna,Andrey Koucheryavy,Ahmed A. Abd El-Latif +5 more
TL;DR: In this article , the authors provide an overview of pedestrian detection issues and the recent advances made in addressing them with the help of DL techniques, with the aim of offering insights to the readers and motivating new research directions.
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Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
TL;DR: This paper suggests a paradigm shift towards cross-dataset evaluation, for the next generation of pedestrian detectors, and proposes a progressive fine-tuning strategy which improves generalization.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
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Proceedings ArticleDOI
Multi-view 3D Object Detection Network for Autonomous Driving
TL;DR: This paper proposes Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes and designs a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths.
Book ChapterDOI
Ten Years of Pedestrian Detection, What Have We Learned?
TL;DR: This work analyzes the remarkable progress of the last decade by dis- cussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark to find a new decision forest detector.