Hierarchical pedestrian detection under low resolution scenario

2012 ◽  
Author(s):  
Yun-Fu Liu ◽  
Jing-Ming Guo ◽  
Che-Hao Chang ◽  
Chih-Hsien Hsia
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution

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.

Low resolution pedestrian detection using light robust features and hierarchical system

2014 ◽  
Vol 47 (4) ◽  
pp. 1616-1625 ◽  
Author(s):  
Yun-Fu Liu ◽  
Jing-Ming Guo ◽  
Che-Hao Chang
Keyword(s):  
Pedestrian Detection ◽  
Hierarchical System ◽  
Low Resolution

scholarly journals Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 780 ◽  
Author(s):  
Chao Zhu ◽  
Xu-Cheng Yin
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution ◽  
Specific Test ◽  
Adaboost Algorithm ◽  
Detection Approach ◽  
Major Bottleneck ◽  
Boosting Algorithm ◽  
Test Sets ◽  
Late Stages ◽  
Possible Cause

Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.

Near Zone Pedestrian Detection using a Low-Resolution FIR Sensor

2007 ◽  
Author(s):  
J.-E. Kallhammer ◽  
D. Eniksson ◽  
G. Granlund ◽  
M. Felsberg ◽  
A. Moe ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution ◽  
Near Zone

scholarly journals Analysis of Feature Fusion Based on HIK SVM and Its Application for Pedestrian Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Song-Zhi Su ◽  
Shu-Yuan Chen
Keyword(s):  
Near Infrared ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Visual Images ◽  
Support Vector ◽  
Fusion Method ◽  
Low Resolution ◽  
Night Time ◽  
Histogram Of Oriented Gradient ◽  
Histogram Intersection

This work presents the fusion of integral channel features to improve the effectiveness and efficiency of pedestrian detection. The proposed method combines the histogram of oriented gradient (HOG) and local binary pattern (LBP) features by a concatenated fusion method. Although neural network (NN) is an efficient tool for classification, the time complexity is heavy. Hence, we choose support vector machine (SVM) with the histogram intersection kernel (HIK) as a classifier. On the other hand, although many datasets have been collected for pedestrian detection, few are designed to detect pedestrians in low-resolution visual images and at night time. This work collects two new pedestrian datasets—one for low-resolution visual images and one for near-infrared images—to evaluate detection performance on various image types and at different times. The proposed fusion method uses only images from the INRIA dataset for training but works on the two newly collected datasets, thereby avoiding the training overhead for cross-datasets. The experimental results verify that the proposed method has high detection accuracies even in the variations of image types and time slots.

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