Study on vehicle front pedestrian detection based on 3D laser scanner

Jin, Lisheng; Niu, Qingning; Hou, Haijing; Shunxi, Hu; Wang, Fangrong

doi:10.1109/tmee.2011.6199307

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The average of all measures for the proposed system has been listed in Table2. To give an average estimate of the performance of the LOP-CNN system, the results have also been verified against two other existing algorithms: that is Template Matching [32] and Vision-based system and PERCLOS Eye openeness [33]. To show the ability of the proposed system to extract local features in a better way for the variation in Driver Warning System, the proposed LOP-CNN method is compared with Template Matching and Vision-based system and PERCLOS Eye openeness.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Suchitra*¹,

Priya²,

Poovaraghan³

et al. 2019

IJRTE

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Driver’s inattention is one of the major factors and reasons in occurrence of many road accidents and unforeseen crashes. Hence it is crucial to develop an automatic driver warning system that can send timely warning signals to the drivers. This issue involves determining the driver’s mental state that is ultimately based on the driver’s facial expressions. Automated facial emotion recognition is a recent development in the image processing domain and is the need of the hour in applications like driver warning systems. The existing methods are capable of recognizing facial emotions even when provided with a noisy signal or imperfect data, but ultimately it lacks accuracy. It is also ineffective in dealing with spontaneous emotions, and recognition. The proposed approach develops a driver warning system that extracts the facial expressions based on a novel efficient Local Octal Pattern (LOP) and effectively recognizes the facial expressions based on Deep Neural Networks, Convolutional Neural Networks (CNN). The LOP feature map serves as an input to CNN and guides in the selection of CNN learning data thereby improving and further enhancing the understanding and learning of CNN. It also has an ability to recognize both natural and spontaneous emotions, as well as image and video can be considered as an input.The experimental results consideringYawDD dataset indicates that the proposed system has been efficiently evaluated by considering the with metrics such as Precision, Recall and F-Score and thereby it is observed and inferred that the proposed system obtained a high recall rate of 96.09% in comparison with the other state-of-the-art methods

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Section: Resultsmentioning

confidence: 99%

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Suchitra*¹,

Priya²,

Poovaraghan³

et al. 2019

IJRTE

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show abstract

“…Although there are many technical testing methods, only relying on appropriate testing technology using by law enforcement officers is not enough due to the limited human and material resources, and diver lacking of some relevant concepts on fatigue driving. Each year around the world road traffic accidents causes road safety problem, and has been greatly threatened the life and property of social public [1][2][3]. Among the many traffic accidents, driver fatigue is the main causes; Klauer et al found that the probability of traffic accidents due to driving fatigue is 4 to 6 times compared to normal driving [4], so it has become the focus of global attention.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Applications of Emotion Detection in Driving Fatigue

Hongzhe¹,

Gongzhang²

2015

TOAUTOCJ

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For the driver at the long-term driving or at the poor mental state, the emotion based driving fatigue recognition algorithm is proposed, and we use the algorithm to effectively prevent the continuous driving of the driver in the state of fatigue, so as to avoid the accident. We sample the facial images of the driver and establish their feature space, and compare them with the fatigue training samples, achieve in real time the driving category attributes of the driver. According to the driving category attributes, we judge whether the driver is in the state of fatigue so as to appropriately intervene in the behavior of the driver.

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“…The detection of pedestrians has been investigated in several studies (ENZWEILER; GAVRILA, 2009; R. OMRAN M.; SCHIELE, 2015; ZHANG R. BENENSON;SCHIELE, 2016;GAVRILA;MUNDER, 2007). Most of them use images (BERTOZZI et al, 2015;YE;JIAO, 2012), 3D point clouds (MEISSNER;DIETMAYER, 2012;JIN et al, 2011;WENG et al, 2020), or even the fusion of both sets of information (LIN; LEE, 2016; SCHLOSSER; CHOW; KIRA, 2016). Li et al (LI et al, 2017b) designed a system for concurrently detecting pedestrians and cyclists.…”

Section: Human Motion Forecastingmentioning

confidence: 99%

Scene compliant spatio-temporal multi-modal multi-agent long-term trajectory forecasting

Ridel¹

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Study on vehicle front pedestrian detection based on 3D laser scanner

Cited by 6 publications

References 10 publications

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Recognition and Applications of Emotion Detection in Driving Fatigue

Scene compliant spatio-temporal multi-modal multi-agent long-term trajectory forecasting

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