Research on Low-Resolution Pedestrian Detection Algorithms based on R-CNN with Targeted Pooling and Proposal

2018 ◽  
Author(s):  
Peng Shi ◽  
Jun Wu ◽  
Kai Wang ◽  
Yao Zhang ◽  
Jiapei Wang ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution ◽  
Detection Algorithms

scholarly journals A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1240
Author(s):  
Yang Liu ◽  
Hailong Su ◽  
Cao Zeng ◽  
Xiaoli Li
Keyword(s):  
Detection Method ◽  
Selection Process ◽  
Pedestrian Detection ◽  
Thermal Infrared ◽  
Single Shot ◽  
Detection Algorithms ◽  
Complex Scenes ◽  
Thermal Infrared Images ◽  
Online Feature Selection ◽  
Huge Challenge

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).

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 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.

scholarly journals AN ALGORITHM FOR PEDESTRIAN DETECTION IN MULTISPECTRAL IMAGE SEQUENCES

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 73-77 ◽  
Author(s):  
V. V. Kniaz ◽  
V. V. Fedorenko
Keyword(s):  
Optical Flow ◽  
Pedestrian Detection ◽  
Obstacle Detection ◽  
Multispectral Image ◽  
Low Light ◽  
Detection Algorithms ◽  
Optical Flows ◽  
The Difference ◽  
Self Driving Cars

The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

scholarly journals Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 255
Author(s):  
Xin Gao ◽  
Sundaresh Ram ◽  
Rohit C. Philip ◽  
Jeffrey J. Rodríguez ◽  
Jeno Szep ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Object Detection ◽  
Aerial Imagery ◽  
Wide Area ◽  
Small Object ◽  
Post Processing ◽  
Low Resolution ◽  
Processing Scheme ◽  
Detection Algorithms

In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

scholarly journals An Improved Pedestrian Detection Algorithm using Integration of Resnet and Yolo V2

2020 ◽  
Vol 9 (1) ◽  
pp. 2480-2485
Keyword(s):  
Feature Extraction ◽  
Pedestrian Detection ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Efficient Algorithms ◽  
Accuracy Rate ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Proposed Model ◽  
Real Time Applications

Pedestrian detection is one of the important tasks in object detection technology. The pedestrian detection algorithm has been used in applications like intelligent video surveillance, traffic analysis, and autonomous driving. In recent years, many pedestrian detection algorithms have been proposed but the key drawback is the accuracy and speed, which can be improved my integrating efficient algorithms. The proposed model improves the pedestrian detection algorithm by integrating two efficient algorithms together. The model is developed using the joint version of ResNet and YOLO v2, which preforms feature extraction and classification respectively. By using this model the efficiency of the system is increased by improving the accuracy rate so that it can be used with real-time applications. The model has been compared with existing models like SSD, Faster R-CNN and Mask R-CNN. Comparing with these models, the proposed model provides mAP value higher than these existing models with less loss function when tested on the INRIA dataset.

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
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