Pedestrian detection with convolutional neural networks

Szarvas, Máté; Yoshizawa, Atsushi; Yamamoto, Makoto; Ogata, Jun

doi:10.1109/ivs.2005.1505106

Cited by 155 publications

(67 citation statements)

References 17 publications

(3 reference statements)

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“…The computational costs are often too high to allow for real-time processing [11], [12], [48], [53], [60], [68]. Significant speedups can be obtained by either coupling the sliding window approach with a classifier cascade of increasing complexity [45], [52], [63], [71], [74], [76], [80], [83] or by restricting the search space based on known camera geometry and prior information about the target object class.…”

Section: Roi Selectionmentioning

confidence: 99%

“…Likewise, the particular configuration of spatial features has been included in the actual optimization itself, yielding feature sets that adapt to the underlying data set during training. Such features are referred to as local receptive fields [19], [23], [49], [68], [75], in reference to neural structures in the human visual cortex [24]. Recent studies have empirically demonstrated the superiority of adaptive local receptive field features over nonadaptive Haar wavelet features with regard to pedestrian classification [49], [68].…”

Section: Discriminative Modelsmentioning

confidence: 99%

“…Optimality of the decision boundary is assessed by minimizing an error criterion with respect to the network parameters, i.e., mean squared error [33]. In the context of pedestrian detection, multilayer neural networks have been applied particularly in conjunction with adaptive local receptive field features as nonlinearities in the hidden network layer [19], [23], [49], [68], [75]. This architecture unifies feature extraction and classification within a single model.…”

Section: Discriminative Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Monocular Pedestrian Detection: Survey and Experiments

Enzweiler

Gavrila

2009

IEEE Trans. Pattern Anal. Mach. Intell.

1,107

569

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Abstract-Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade [74], HOG/linSVM [11], NN/LRF [75], and combined shape-texture detection [23]. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.

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Section: Roi Selectionmentioning

confidence: 99%

Section: Discriminative Modelsmentioning

confidence: 99%

Section: Discriminative Modelsmentioning

confidence: 99%

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Monocular Pedestrian Detection: Survey and Experiments

Enzweiler

Gavrila

2009

IEEE Trans. Pattern Anal. Mach. Intell.

1,107

569

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show abstract

Section: Introductionmentioning

confidence: 99%

“…This paper presents a pedestrian candidate region segmentation method based on the integration of the shape characteristics of the pedestrian and AdaBoost algorithm, extract the possible position of the pedestrians in the image, so that provide input for the effective identification of pedestrian. [1,2] Improved AdaBoost algorithm Improved sample weights update method. FPR: the number of negative samples that be wrongly classified into positive samples with the ratio of the total number of negative samples, means the misclassification rate of negative samples; FNR: the number of positive samples that be wrongly classified into negative samples with the ratio of the total number of positive samples, means the misclassification rate of positive samples.…”

Section: Introductionmentioning

confidence: 99%

Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

Ying

Guo

et al. 2011

AEF

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Abstract. In this paper, propose a pedestrian detection method that based on AdaBoost algorithm and pedestrian shape features integration. First according to the collected pedestrian true, false sample, selected the characteristics of the extended class Haar, adopt AdaBoost algorithm training get pedestrian classifier to split the initial candidate region of all pedestrians in the image. In this paper, propose an adaptive threshold weight update method, significantly reduced the number of the characteristics of strong classifier, optimize the classifier structure, reduce the complexity of the algorithm; meanwhile, the online update detector, improving the reliability of the detector. Pedestrian leg have strong vertical edge symmetry characteristic so that extracted the vertical edge detection in the initial candidate region, According to the symmetry determine the vertical axis of symmetry, combined with the morphological characteristics of pedestrians to determine the width and height characteristics of the pedestrian, to determine the pedestrian candidate region, Finally, put a further validation to the pedestrian candidate region. IntroductionWith China's rapid increase in car ownership, frequency of road traffic accidents, especially the traffic accidents caused by vehicle and pedestrian collision, is the main reason for pedestrian casualties. It makes the application of safety driver assistance systems in our country with greater urgency and relevance. Detection of the pedestrian in front of the vehicle is one the Indispensable functions of the safety driver assistance systems. It can effectively help the driver timely respond to the external environment in the urban to avoid pedestrian collisions. For a pedestrian detection system, it should have a good real-time, low false alarm rate, good environment adaptability. Currently, the commonly used pedestrian segmentation method is edge detection, is mainly to split the pedestrian in road. This paper presents a pedestrian candidate region segmentation method based on the integration of the shape characteristics of the pedestrian and AdaBoost algorithm, extract the possible position of the pedestrians in the image, so that provide input for the effective identification of pedestrian. [1,2]

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LIDAR and vision‐based pedestrian detection system

Premebida

Ludwig

Nunes

2009

Journal of Field Robotics

171

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A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with singlebased classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies.C 2009 Wiley Periodicals, Inc.

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Pedestrian detection with convolutional neural networks

Cited by 155 publications

References 17 publications

Monocular Pedestrian Detection: Survey and Experiments

Monocular Pedestrian Detection: Survey and Experiments

Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

LIDAR and vision‐based pedestrian detection system

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