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Research and Design on Vehicle Pedestrian Detection in Natural Scene

Pei-ran ZHAO, Xue-wu ZHANG, Yu-bo XIE, Ling-li XU, Yan XIANG, Jin-bao SHENG
2018 DEStech Transactions on Computer Science and Engineering  
We propose a two-layer pedestrian feature extraction algorithm based on multi features fusion in integral channel, which is gained by intelligent driving system in natural environment.  ...  While ensuring real-time detection, we improved the low robustness of single layer features.  ...  Summary This paper makes improvement based on integral image technology, presents a double layer pedestrian detection algorithm based on multi feature fusion is a kind of integral channel.  ... 
doi:10.12783/dtcse/cmee2017/20042 fatcat:dxi5ixajljdpfm563kgh6kfik4

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Shafiza Ariffin Kashinath, Salama A. Mostafa, Aida Mustapha, Hairulnizam Mahdin, David Lim, Moamin A. Mahmoud, Mazin Abed Mohammed, Bander Ali Saleh Al-rimy, Mohd Farhan Md Fudzee, Tan Jhon Yang
2021 IEEE Access  
[38] suggest a hybrid method based on deep feature fusion modeling to achieve speed prediction.  ...  [74] propose a robust travel-time estimation method based on license plate recognition, geomagnetic detector data, and floating car data as traffic data input. Zhang et al.  ... 
doi:10.1109/access.2021.3069770 fatcat:2p52c7psrzhofgy4l2jy5fxw4u

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Amira Mimouna, Ihsen Alouani, Anouar Ben Khalifa, Yassin El Hillali, Abdelmalik Taleb-Ahmed, Atika Menhaj, Abdeldjalil Ouahabi, Najoua Essoukri Ben Amara
2020 Electronics  
Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities.  ...  In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception.  ...  Afterwards, features are concatenated via VoxelNet architecture. In [33] an architecture based on two single stage detector is proposed.  ... 
doi:10.3390/electronics9040560 fatcat:m7xosqzegbgfnabzqzp3n2gagy

Belief Function Definition for Ensemble Methods - Application to Pedestrian Detection in Dense Crowds

Jennifer Vandoni, Sylvie Le Hegarar-Mascle, Emanuel Aldea
2018 2018 21st International Conference on Information Fusion (FUSION)  
Fusion In order to perform the fusion of detectors based on different features, there exist in the literature various approaches depending on the considered problem.  ...  In the context of high-density crowd pedestrian detection, in [3] we propose a robust fusion strategy also based on the belief functions framework, that is able to take into account the spatial imprecision  ... 
doi:10.23919/icif.2018.8455313 dblp:conf/fusion/VandoniHA18 fatcat:5zvr6quhevgfjcl5hpfzxlwcdq

Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors

Guang Chen, Hu Cao, Canbo Ye, Zhenyan Zhang, Xingbo Liu, Xuhui Mo, Zhongnan Qu, Jörg Conradt, Florian Röhrbein, Alois Knoll
2019 Frontiers in Neurorobotics  
We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736  ...  In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies.  ...  It is expected that the pedestrian detector based on such merged data could enjoy better adaptability and robustness with a higher average detection precision.  ... 
doi:10.3389/fnbot.2019.00010 pmid:31001104 pmcid:PMC6454154 fatcat:ghcpudwcjjdixny2ysjvondyx4

Robust Deep Multi-modal Learning Based on Gated Information Fusion Network [article]

Jaekyum Kim, Junho Koh, Yecheol Kim, Jaehyung Choi, Youngbae Hwang, Jun Won Choi
2018 arXiv   pre-print
improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes.  ...  In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps  ...  In Section 2, we review the previous literature on the DML. In Section 3, we present the details on the proposed GIF network and the robust 2D object detector based on multi-modal fusion.  ... 
arXiv:1807.06233v2 fatcat:hf24etcq6be4bjjfisk36wqdlm

An evidential framework for pedestrian detection in high-density crowds

Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat-Mascle
2017 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)  
We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors.  ...  Moreover, the proposed algorithm outperforms a fusion solution based on Multiple Kernel Learning on difficult high-density crowd images acquired at Makkah at the height of the Muslim pilgrimage.  ...  Fusion Regarding the fusion of detectors based on different features, some fundamentally different approaches are popular in the literature for the pedestrian detection task.  ... 
doi:10.1109/avss.2017.8078498 dblp:conf/avss/VandoniAH17 fatcat:koncadx2cjekllzfn6tvqn3tdq

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection [article]

Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan Yuille (+1 others)
2022 arXiv   pre-print
Based on InverseAug and LearnableAlign, we develop a family of generic multi-modal 3D detection models named DeepFusion, which is more accurate than previous methods.  ...  However, as those features are often augmented and aggregated, a key challenge in fusion is how to effectively align the transformed features from two modalities.  ...  Based on these techniques, we develop a family of simple, generic, yet effective multi-modal 3D detectors, named DeepFusions, which achieves state-of-theart performance on the Waymo Open Dataset. on 3D  ... 
arXiv:2203.08195v1 fatcat:ehhtpds7cvexrnnlw5hccmegbq

Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF

Bassem Besbes, Alexandrina Rogozan, Adela-Maria Rus, Abdelaziz Bensrhair, Alberto Broggi
2015 Sensors  
Our system consists of three modules, each based on speeded-up robust feature (SURF) matching.  ...  In this paper, we propose a pedestrian detector with on-board FIR camera.  ...  The plotting results on our testing dataset show that our detector based on the HC of SURF features (HCS) outperforms the state-of-the-art pedestrian detectors: VJ, HOG and MultiFtr.  ... 
doi:10.3390/s150408570 pmid:25871724 pmcid:PMC4431237 fatcat:5s44rufhdbdytjyxb4uvzibz3i

MLOD: A multi-view 3D object detection based on robust feature fusion method

Jian Deng, Krzysztof Czarnecki
2019 2019 IEEE Intelligent Transportation Systems Conference (ITSC)  
Hence the object detector can be trained on data labelled in different views to avoid the degeneration of feature extractors.  ...  The fusion of image and BEV features is challenging, as they are derived from different perspectives.  ...  [20] proposed a method that projects image features into BEV and fuses them with the convolutional layers of a LIDAR based detector using a continuous fusion layer (see Figure 2 .13).  ... 
doi:10.1109/itsc.2019.8917126 dblp:conf/itsc/DengC19 fatcat:kp7ablx24bfzvev5hihvcc6pcq

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Junhwan Ryu, Sungho Kim
2019 Journal of Sensors  
This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size.  ...  The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20%  ...  Figure 9 : 9 Figure 9: Comparison of the results of proposed deep learning based detector, conventional CFAR detector, and HB-based detector.The proposed detector is based on the fusion method using two  ... 
doi:10.1155/2019/4658068 fatcat:b26vferrlzeyblj4ijooulp7u4

Person search: New paradigm of person re-identification: A survey and outlook of recent works

Khawar Islam
2020 Image and Vision Computing  
This survey paper includes brief discussion about feature representation learning and deep metric learning with novel loss functions.  ...  We thoroughly review datasets with performance analysis on existing datasets. Finally, we are reviewing current solutions for further consideration.  ...  To focus on those areas where pedestrian gather in a scene, pedestrian detector Faster-RCNN modified and implemented structure-aware anchors in detector.  ... 
doi:10.1016/j.imavis.2020.103970 fatcat:g2zuqww7tbdszkxrc2wkrfno2y

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection [article]

Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe
2018 arXiv   pre-print
Our approach relies on a novel cross-modality learning framework and it is based on two main phases.  ...  In this way, features which are both discriminative and robust to bad illumination conditions are learned.  ...  Differently from previous deep learning models addressing the occlusion problem, DeepParts does not rely on a single detector but it is based on multiple part detectors. Tian et al.  ... 
arXiv:1704.02431v2 fatcat:keuvqg4dfzbu3e6h4eemrcj34e

Query Adaptive Late Fusion for Image Retrieval [article]

Zhongdao Wang, Liang Zheng, Shengjin Wang
2018 arXiv   pre-print
In the learning version, it can also be applied to supervised tasks like person recognition and pedestrian retrieval, based on a trainable neural module.  ...  Feature fusion is a commonly used strategy in image retrieval tasks, which aggregates the matching responses of multiple visual features.  ...  We use the part-based convolutional baseline (PCB) [31] as the pedestrian descriptor, which outputs six part-based features for a pedestrian image.  ... 
arXiv:1810.13103v1 fatcat:3rvbbzxgg5cvbmmvz4ar3jlsmq

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Our approach relies on a novel cross-modality learning framework and it is based on two main phases.  ...  In this way, features which are both discriminative and robust to bad illumination conditions are learned.  ...  Differently from previous deep learning models addressing the occlusion problem, DeepParts does not rely on a single detector but it is based on multiple part detectors. Tian et al.  ... 
doi:10.1109/cvpr.2017.451 dblp:conf/cvpr/XuORWS17 fatcat:dx3gluuoffdklouxprura6zaty
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