Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes
Tài liệu tham khảo
S. Liu, D. Huang, Y. Wang, Adaptive nms: Refining pedestrian detection in a crowd, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 6459–6468.
Boukerche, 2021, Design guidelines on deep learning–based pedestrian detection methods for supporting autonomous vehicles, ACM Comput. Surv., 54, 1, 10.1145/3460770
S. Zhang, L. Wen, X. Bian, Z. Lei, S.Z. Li, Occlusion-aware R-CNN: detecting pedestrians in a crowd, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 637–653.
Shao, 2018
Zhou, 2018, Robust mobile crowd sensing: When deep learning meets edge computing, Ieee Netw., 32, 54, 10.1109/MNET.2018.1700442
Chaudhary, 2022, Video based human crowd analysis using machine learning: a survey, Comput. Methods Biomech. Biomed. Eng.: Imaging Visualization, 10, 113
Hasan, 2022
Tan, 2022, 3D sensor based pedestrian detection by integrating improved HHA encoding and two-branch feature fusion, Remote Sens., 14, 645, 10.3390/rs14030645
Tang, 2022, Multi-expert learning for fusion of pedestrian detection bounding box, Knowl.-Based Syst., 241, 10.1016/j.knosys.2022.108254
Chen, 2021, Deep neural network based vehicle and pedestrian detection for autonomous driving: A survey, IEEE Trans. Intell. Transp. Syst., 22, 3234, 10.1109/TITS.2020.2993926
Y. Xu, Z. Piao, S. Gao, Encoding crowd interaction with deep neural network for pedestrian trajectory prediction, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5275–5284.
Dasgupta, 2022, Spatio-contextual deep network-based multimodal pedestrian detection for autonomous driving, IEEE Trans. Intell. Transp. Syst., 10.1109/TITS.2022.3146575
Cormier, 2021, Fast pedestrian detection for real-world crowded scenarios on embedded gpu, 40
Wang, 2022, Pyramid-dilated deep convolutional neural network for crowd counting, Appl. Intell., 52, 1825, 10.1007/s10489-021-02537-6
T. Song, L. Sun, D. Xie, H. Sun, S. Pu, Small-scale pedestrian detection based on topological line localization and temporal feature aggregation, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 536–551.
Khalifa, 2020, A novel multi-view pedestrian detection database for collaborative intelligent transportation systems, Future Gener. Comput. Syst., 113, 506, 10.1016/j.future.2020.07.025
Xie, 2021, Occluded pedestrian detection techniques by deformable attention-guided network (DAGN), Appl. Sci., 11, 6025, 10.3390/app11136025
Li, 2021, Conditional random fields as message passing mechanism in anchor-free network for multi-scale pedestrian detection, Inform. Sci., 550, 1, 10.1016/j.ins.2020.10.049
Joshi, 2021, Ensemble of deep learning-based multimodal remote sensing image classification model on unmanned aerial vehicle networks, Mathematics, 9, 2984, 10.3390/math9222984
Ma, 2022, An improved ResNet-50 for garbage image classification, Tehnički Vjesnik, 29, 1552
Rizwan, 2022, Alpha harris hawks optimization based overcurrent relay coordination with hybrid time-current-voltage characteristics considering the grid-connected distributed generation, J. Eng. Res., 10.36909/jer.ICEPE.19505
Shahkarami, 2022, Complexity reduction over bi-RNN-based nonlinearity mitigation in dual-pol fiber-optic communications via a CRNN-based approach, Opt. Fiber Technol., Mater. Devices Syst., 74, 10.1016/j.yofte.2022.103072
Liu, 2022, A fuzzy-based method for cloud service migration using a shark smell optimization algorithm, Concurr. Comput.: Pract. Exper.
Kim, 2020, Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance, Soft Comput., 24, 17081, 10.1007/s00500-020-04999-1