Detection of pedestrians at far distance

Bunel, Rudy; Davoine, Franck; Xu, Philippe

doi:10.1109/icra.2016.7487382

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…Bunel et al [16] focused on remote pedestrian detection. When the pedestrian is too far from the camera, the size of the pedestrian becomes very small, so it becomes very difficult to detect.…”

Section: Studies Conducted In the Field Of Pedestrian Identification For The Development Of Intelligent Transportation Systems Andmentioning

confidence: 99%

Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

Olia

Oskouei

et al. 2021

Journal of Advanced Transportation

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Intelligent transportation systems have been very well received by car companies, people, and governments around the world. The main challenge in the world of smart and self-driving cars is to identify obstacles, especially pedestrians, and take action to prevent collisions with them. Many studies in this field have been done by various researchers, but there are still many errors in the accurate detection of pedestrians in self-made cars made by different car companies, so in the research in this study, we focused on the use of deep learning techniques to identify pedestrians for the development of intelligent transportation systems and self-driving cars and pedestrian identification in smart cities, and then some of the most common deep learning techniques used by various researchers were reviewed. Finally, in this research, the challenges in each field are discovered, which can be very useful for students who are looking for an idea to do their dissertations and research in the field of smart transportation and smart cities.

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Section: Studies Conducted In the Field Of Pedestrian Identification For The Development Of Intelligent Transportation Systems Andmentioning

confidence: 99%

Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

Olia

Oskouei

et al. 2021

Journal of Advanced Transportation

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“…A deep, unified pattern that conjointly learns feature extraction, deformation handling, occlusion handling, and classification evaluated on the Caltech and the ETH datasets for pedestrian detection was proposed in [7]. An investigation focused on the detection of small scale pedestrians on the Caltech data set connected with a CNN learning of features with an end-to-end approach was presented in [8].…”

Section: Related Workmentioning

confidence: 99%

Detection of Pedestrian Actions Based on Deep Learning Approach

Pop¹

2019

Studia UBB Informatica

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The pedestrian detection has attracted considerable attention from research due to its vast applicability in the field of autonomous vehicles. In the last decade, various investigations were made to find an optimal solution to detect the pedestrians, but less of them were focused on detecting and recognition the pedestrian's action. In this paper, we converge on both issues: pedestrian detection and pedestrian action recognize at the current detection time (T=0) based on the JAAD dataset, employing deep learning approaches. We propose a pedestrian detection component based on Faster R-CNN able to detect the pedestrian and also recognize if the pedestrian is crossing the street in the detecting time. The method is in contrast with the commonly pedestrian detection systems, which only discriminate between pedestrians and non-pedestrians among other road users.

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“…In [11] the authors present a mixtureof-experts framework performed with HOG, LBP features and MLP or linear SVM classifiers. Recently, in [12] a CNN to learn the features with an end-to-end approach was presented. This experiment focused on the detection of small scale pedestrians on the Caltech data set.…”

Section: Previous Workmentioning

confidence: 99%

Incremental Cross-Modality deep learning for pedestrian recognition

Pop

Rogozan

Nashashibi

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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In spite of the large number of existing methods, pedestrian detection remains an open challenge. In recent years, deep learning classification methods combined with multimodality images within different fusion schemes have achieved the best performance. It was proven that the late-fusion scheme outperforms both direct and intermediate integration of modalities for pedestrian recognition. Hence, in this paper, we focus on improving the late-fusion scheme for pedestrian classification on the Daimler stereo vision data set. Each image modality, Intensity, Depth and Flow, is classified by an independent Convolutional Neural Network (CNN), the outputs of which are then fused by a Multi-layer Perceptron (MLP) before the recognition decision. We propose different methods based on Cross-Modality deep learning of CNNs: (1) a correlated model where a unique CNN is trained with Intensity, Depth and Flow images for each frame, (2) an incremental model where a CNN is trained with the first modality images frames, then a second CNN, initialized by transfer learning on the first one is trained on the second modality images frames, and finally a third CNN initialized on the second one, is trained on the last modality images frames. The experiments show that the incremental cross-modality deep learning of CNNs improves classification performances not only for each independent modality classifier, but also for the multi-modality classifier based on late-fusion. Different learning algorithms are also investigated.

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Detection of pedestrians at far distance

Cited by 8 publications

References 29 publications

Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

Detection of Pedestrian Actions Based on Deep Learning Approach

Incremental Cross-Modality deep learning for pedestrian recognition

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