Pedestrian Detection in Low-Resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)

2014 ◽  
Author(s):  
Jiejie Zhu ◽  
Omar Javed ◽  
Jingen Liu ◽  
Qian Yu ◽  
Hui Cheng ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution ◽  
Multi Scale ◽  
Intrinsic Motion

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.

Enhanced Cascading Classifier Using Multi-Scale HOG for Pedestrian Detection from Aerial Images

2016 ◽  
Vol 30 (03) ◽  
pp. 1655009 ◽  
Author(s):  
Daisuke Sugimura ◽  
Takayuki Fujimura ◽  
Takayuki Hamamoto
Keyword(s):  
Online Learning ◽  
Deep Structure ◽  
Pedestrian Detection ◽  
Aerial Images ◽  
Low Resolution ◽  
Edge Information ◽  
Multi Scale ◽  
Trained Classifier ◽  
Training Samples ◽  
Input Layer

We propose a method for pedestrian detection from aerial images captured by unmanned aerial vehicles (UAVs). Aerial images are captured at considerably low resolution, and they are often subject to heavy noise and blur as a result of atmospheric influences. Furthermore, significant changes to the appearance of pedestrians frequently occur because of UAV motion. In order to address these crucial problems, we propose a cascading classifier that concatenates a pre-trained classifier and an online learning-based classifier. We construct the first classifier using deep belief network (DBN) with an extended input layer. Unlike previous approaches that use raw images as the input layer of the DBN, we exploit multi-scale histogram of oriented gradients (MS-HOG) features. The MS-HOG enables us to supply better and richer information than low-resolution aerial images for constructing a reliable deep structure of DBN, because the dimensions of the input features can be expanded. Furthermore, the MS-HOG effectively extracts the necessary edge information while reducing trivial gradients and noise. The second classifier is based on online learning, and it uses predictions of the target appearance using UAV motions. Predicting the target appearance enables us to collect reliable training samples for the classifier’s online learning process. Experiments using aerial videos demonstrate the effectiveness of the proposed method.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

Multi-Scale Infrared Pedestrian Detection Based on Deep Attention Mechanism

2020 ◽  
Vol 40 (5) ◽  
pp. 0504001
Author(s):  
赵斌 Zhao Bin ◽  
王春平 Wang Chunping ◽  
付强 Fu Qiang ◽  
陈一超 Chen Yichao
Keyword(s):  
Pedestrian Detection ◽  
Attention Mechanism ◽  
Multi Scale

A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

2020 ◽  
pp. 1-13
Author(s):  
Peiyu Yang ◽  
Guofeng Zhang ◽  
Lu Wang ◽  
Lisheng Xu ◽  
Qingxu Deng ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale
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