Results 311 to 320 of about 4,817,825 (346)

Pedestrian Detection in Crowded Scenes

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005
In this paper, we address the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. Our basic premise is that this problem is too difficult for any type of model or feature alone. Instead, we present an algorithm that integrates evidence in multiple iterations and from different sources.
Bastian Leibe, Edgar Seemann, Bernt Schiele   +2 more
openaire   +2 more sources

Accuracy prediction for pedestrian detection

2017 IEEE International Conference on Image Processing (ICIP), 2017
In this paper, we address the problem of predicting accuracy for pedestrian detection. We want to be able to predict the accuracy of a video analytic method without actually executing the method. We propose the use of texture descriptors and random forests to predict the accuracy of various pedestrian detection methods.
Khalid Tahboub, Amy R. Reibman, Edward J. Delp   +2 more
openaire   +1 more source

Neural features for pedestrian detection

Neurocomputing, 2017
This paper presents a pedestrian detection approach that uses neural features from a fully convolutional network (FCN) instead of features manually designed. We train an AdaBoost detector per layer and compare the performance to find the optimal layer for this task. Combining results of multiple detectors can further improve the performance.
Chao Li 0007, Xinggang Wang, Wenyu Liu 0001   +2 more
openaire   +1 more source

Simultaneously detect and segment pedestrian

2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2013
We present a framework to simultaneously detect and segment pedestrian in images. Our work is based on part-based method. We first segment the image into superpixels, then assemble superpixels into body part candidates by comparing the assembled shape with pre-built template library.
Shu Wang, Zhenjiang Miao, Jian Zhang 0002   +2 more
openaire   +1 more source

A Novel Architecture of Pedestrian Detection

2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019
In the past decade, the pedestrian detection has drawn much attention due to the significant role it plays in artificial intelligence system and vehicle assisted driving system. In order to achieve a balance between recognition rate and detection time, a novel architecture of pedestrian detection has been proposed in this paper.
Wenshu Li, Menghui Ruan, Xiaoying Guo, Hongyan Wang, Hancui Hancui, Yang Han   +5 more
openaire   +1 more source

High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection

Computer Vision and Pattern Recognition, 2019
Object detection generally requires sliding-window classifiers in tradition or anchor-based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in windows or anchors.
W. Liu, Shengcai Liao, Weiqiang Ren, Weidong Hu, Yinan Yu   +4 more
semanticscholar   +1 more source

Pedestrian Detection in Nighttime Driving

Third International Conference on Image and Graphics (ICIG'04), 2005
This paper presents an approach for pedestrian detection in the nighttime driving with a normal camera. Bright objects in the video are extracted with an adaptive thresholding segmentation algorithm. Then, the size, position, and shape of each object are analyzed to judge whether it is a pedestrian.
Q. M. Tian, Y. P. Luo, D. C. Hu
openaire   +1 more source

Spatial Attention for Pedestrian Detection

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019
Achieving high detection accuracy and high inference speed is important for a pedestrian detection system in self-driving applications. There exists a trade-off between detection accuracy and inference speed in modern convolu-tional object detectors. In this paper, we propose a novel pedestrian detection system, which leverages spatial attention and a ...
Ujjwal, Ujjwal, Dziri, Aziz, Leroy, Bertrand, Bremond, François   +3 more
openaire   +2 more sources

SCHOG Feature for Pedestrian Detection

Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 2014
Co-occurrence Histograms of Oriented Gradients(CoHOG) has succeeded in describing the detailed shape of the object by using a co-occurrence of features. However, unlike HOG, it does not consider the difference of gradient magnitude between the foreground and the background. In addition, the dimension of the CoHOG feature is also very large.
Ryuichi Ozaki, Kazunori Onoguchi
openaire   +1 more source

Transfer learning for pedestrian detection

Neurocomputing, 2013
Most of the existing methods for pedestrian detection work well, only when the following assumption is satisfied: the features extracted from the training dataset and the testing dataset have very similar distributions in the feature space. However, in practice, this assumption does not hold because of the scene complexity and variation. In this paper,
Xianbin Cao 0001, Zhong Wang 0008, Pingkun Yan, Xuelong Li 0001   +3 more
openaire   +2 more sources