An Extreme Learning Machine-based pedestrian detection method

Yang, Kai; Du, Eliza Yingzi; Delp, Edward J.; Jiang, Pingge; Jiang, Feng; Chen, Yaobin; Sherony, Rini; Takahashi, Hiroyuki

doi:10.1109/ivs.2013.6629663

Cited by 20 publications

(3 citation statements)

References 19 publications

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“…The first step is the automatic detection of pedestrians in the captured video using image processing techniques. Learning-machine based multi-stage pedestrian detection technique has been applied to search for humans in all collected datasets, and please refer to the previous publications [15] [16] for detailed methodologies and detection results.…”

Section: Analysis Of Pedestrian Behaviors Using the Collected Natumentioning

confidence: 99%

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

Tian

Yang

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Modeling vehicle-pedestrian interactions in the road environment is essential to develop pedestrian detection and pedestrian crash avoidance systems. In this paper, one novel approach is proposed to estimate the vehicle-pedestrian encountering risk in the road environment based on a large scale naturalistic driving data collection. Considering the difficulty to record actual pedestrian crashes in the naturalistic data collection, the encountering risk is estimated by the chances for driver to meet with pedestrian in the roadway as well as the chances for the driver and pedestrian to get into a potential conflict. Effects of different scenarios consisting of road conditions, pedestrian behaviors, and pedestrian numbers on the risk levels are also evaluated, and significant results are provided.

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Section: Analysis Of Pedestrian Behaviors Using the Collected Natumentioning

confidence: 99%

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

Tian

Yang

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…The lower body detector is additionally incorporated into the traditional whole body detector to effectively reduce the false positives. More details about our pedestrian detection method can be found in [20,23]. b.…”

Section: Local Pedestrian Clutter Measure a Automatic Pedestrian mentioning

confidence: 99%

A new approach of visual clutter analysis for pedestrian detection

Yang

Delp³

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Detection of pedestrians during driving is a complex cognitive activity. Some of the pedestrian crashes are due to driver's late or difficult perception of pedestrian's appearance. Visual clutter analysis can be used to study the factors that affect human visual search efficiency. In this research, we propose a new approach of visual clutter analysis for pedestrian detection. We design two quantitative measures: the global environment clutter score to capture the complexity of the driving environment in terms of visual search; and the local pedestrian clutter score to evaluate the search efficiency of the pedestrian in the given driving environment. Our preliminary experimental results show that our proposed measures could serve as a reasonable indicator for measuring the human vision perception of pedestrian appearance in naturalistic driving environment.

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“…As an efficient single-hidden-layer feed forward neural network, which has generally good performance and fast learning speed, ELM has been applied in a variety of domains, such as computer vision, energy disaggregation [6], and speech enhancement [7]. In [8], authors proposed a novel pedestrian detection method using multimodal Histogram of Oriented Gradient for pedestrian feature extraction and extreme learning machine for classification to reduce the detection rate of false positives and accelerate processing speed. The experimental results have proved the efficiency of the ELM based method.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios

2019

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Pedestrian attribute recognition is to predict a set of attribute labels of the pedestrian from surveillance scenarios, which is a very challenging task for computer vision due to poor image quality, continual appearance variations, as well as diverse spatial distribution of imbalanced attributes. It is desirable to model the label dependencies between different attributes to improve the recognition performance as each pedestrian normally possesses many attributes. In this paper, we treat pedestrian attribute recognition as multi-label classification and propose a novel model based on the graph convolutional network (GCN). The model is mainly divided into two parts, we first use convolutional neural network (CNN) to extract pedestrian feature, which is a normal operation processing image in deep learning, then we transfer attribute labels to word embedding and construct a correlation matrix between labels to help GCN propagate information between nodes. This paper applies the object classifiers learned by GCN to the image representation extracted by CNN to enable the model to have the ability to be end-to-end trainable. Experiments on pedestrian attribute recognition dataset show that the approach obviously outperforms other existing state-of-the-art methods.

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An Extreme Learning Machine-based pedestrian detection method

Cited by 20 publications

References 19 publications

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

A new approach of visual clutter analysis for pedestrian detection

Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios

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