Efficient Pedestrian Detection in the Low Resolution via Sparse Representation with Sparse Support Regression

2017 ◽  
pp. 313-323
Author(s):  
Wenhua Fang ◽  
Jun Chen ◽  
Ruimin Hu
Keyword(s):  
Sparse Representation ◽  
Pedestrian Detection ◽  
Low Resolution

scholarly journals Super-resolution reconstruction for a single image based on self-similarity and compressed sensing

2018 ◽  
Vol 12 (3) ◽  
pp. 234-244
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Self Similarity ◽  
Resolution Image ◽  
Reconstruction Performance

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image’s self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.

Pedestrian detection from thermal images: A sparse representation based approach

2016 ◽  
Vol 76 ◽  
pp. 157-167 ◽  
Author(s):  
Bin Qi ◽  
Vijay John ◽  
Zheng Liu ◽  
Seiichi Mita
Keyword(s):  
Sparse Representation ◽  
Pedestrian Detection ◽  
Thermal Images

scholarly journals Feature Selection and Pedestrian Detection Based on Sparse Representation

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0134242 ◽  
Author(s):  
Shihong Yao ◽  
Tao Wang ◽  
Weiming Shen ◽  
Shaoming Pan ◽  
Yanwen Chong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Sparse Representation ◽  
Pedestrian Detection

scholarly journals Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

2018 ◽  
Vol 232 ◽  
pp. 02040
Author(s):  
Fuzhen Zhu ◽  
Xin Huang ◽  
Yue Liu ◽  
Haitao Zhu
Keyword(s):  
Remote Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Resolution Image ◽  
Training Images ◽  
Feature Extract ◽  
Resolution Algorithm

In order to obtain higher quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.

scholarly journals Laplace inversion of low‐resolution NMR relaxometry data using sparse representation methods

2013 ◽  
Vol 42 (3) ◽  
pp. 72-88 ◽  
Author(s):  
Paula Berman ◽  
Ofer Levi ◽  
Yisrael Parmet ◽  
Michael Saunders ◽  
Zeev Wiesman
Keyword(s):  
Sparse Representation ◽  
Low Resolution ◽  
Laplace Inversion ◽  
Nmr Relaxometry

scholarly journals Pedestrian Detection at Night in Infrared Images Using an Attention-Guided Encoder-Decoder Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 809 ◽  
Author(s):  
Yunfan Chen ◽  
Hyunchul Shin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pedestrian Detection ◽  
Weather Conditions ◽  
Superior Performance ◽  
Low Resolution ◽  
Feature Maps ◽  
Camera System ◽  
Ir Camera ◽  
Multi Scale

Pedestrian-related accidents are much more likely to occur during nighttime when visible (VI) cameras are much less effective. Unlike VI cameras, infrared (IR) cameras can work in total darkness. However, IR images have several drawbacks, such as low-resolution, noise, and thermal energy characteristics that can differ depending on the weather. To overcome these drawbacks, we propose an IR camera system to identify pedestrians at night that uses a novel attention-guided encoder-decoder convolutional neural network (AED-CNN). In AED-CNN, encoder-decoder modules are introduced to generate multi-scale features, in which new skip connection blocks are incorporated into the decoder to combine the feature maps from the encoder and decoder module. This new architecture increases context information which is helpful for extracting discriminative features from low-resolution and noisy IR images. Furthermore, we propose an attention module to re-weight the multi-scale features generated by the encoder-decoder module. The attention mechanism effectively highlights pedestrians while eliminating background interference, which helps to detect pedestrians under various weather conditions. Empirical experiments on two challenging datasets fully demonstrate that our method shows superior performance. Our approach significantly improves the precision of the state-of-the-art method by 5.1% and 23.78% on the Keimyung University (KMU) and Computer Vision Center (CVC)-09 pedestrian dataset, respectively.

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