Smart City Gnosys
Smart city article details
| Title | Research On Multi-Modal Pedestrian Detection And Tracking Algorithm Based On Deep Learning |
|---|---|
| ID_Doc | 45511 |
| Authors | Zhao R.; Hao J.; Huo H. |
| Year | 2024 |
| Published | Future Internet, 16, 6 |
| DOI | http://dx.doi.org/10.3390/fi16060194 |
| Abstract | In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal daytime lighting, visibility is enhanced, leading to superior pedestrian detection outcomes. Conversely, under low-light conditions, visible light mode imaging falters due to the inadequate provision of pedestrian target information, resulting in a marked decline in detection efficacy. In this context, infrared light mode imaging emerges as a valuable supplement, bolstering pedestrian information provision. This paper delves into pedestrian detection and tracking algorithms within a multi-modal image framework grounded in deep learning methodologies. Leveraging the YOLOv4 algorithm as a foundation, augmented by a channel stack fusion module, a novel multi-modal pedestrian detection algorithm tailored for intelligent transportation is proposed. This algorithm capitalizes on the fusion of visible and infrared light mode image features to enhance pedestrian detection performance amidst complex road environments. Experimental findings demonstrate that compared to the Visible-YOLOv4 algorithm, renowned for its high performance, the proposed Double-YOLOv4-CSE algorithm exhibits a notable improvement, boasting a 5.0% accuracy rate enhancement and a 6.9% reduction in logarithmic average missing rate. This research’s goal is to ensure that the algorithm can run smoothly even on a low configuration 1080 Ti GPU and to improve the algorithm’s coverage at the application layer, making it affordable and practical for both urban and rural areas. This addresses the broader research problem within the scope of smart cities and remote ends with limited computational power. © 2024 by the authors. |
| Author Keywords | deep learning; feature fusion; future internet; multimodal; pedestrian detection; smart cities; YOLOv4 algorithm |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
62125  | 0.88 | Song F.; Li P. | Yolov5-Ms: Real-Time Multi-Surveillance Pedestrian Target Detection Model For Smart Cities | Biomimetics, 8, 6 (2023) |
| 23647 | 0.876 | Bhonde T.; Temare H.; Dadwhal Y.S. | Enhanced Object Detection Using Yolov8: Identifying Vehicles And Pedestrians In Urban Environments | 2024 IEEE Pune Section International Conference, PuneCon 2024 (2024) |
| 15954 | 0.872 | Ansarnia M.S.; Tisserand E.; Berviller Y.; Schweitzer P.; Zidane M.A. | Contextual Detection Of Pedestrians And Vehicles In Orthophotography By Fusion Of Deep Learning Algorithms | Sensors, 22, 4 (2022) |
| 25952 | 0.871 | Sui H.; Han H.; Cui Y.; Yang M.; Pei B. | Fa-Yolo: A Pedestrian Detection Algorithm With Feature Enhancement And Adaptive Sparse Self-Attention | Electronics (Switzerland), 14, 9 (2025) |
| 62128 | 0.867 | Sivaraman G.; Sophiya E.; Diviya M. | Yolov8 For Pedestrian Detection: A Comparative Study For Pedestrian Detection | 2024 3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024 (2024) |
| 17814 | 0.866 | Wang X.; Sun Z.; Chehri A.; Jeon G.; Song Y. | Deep Learning And Multi-Modal Fusion For Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, And Comparative Study | Information Fusion, 105 (2024) |
| 23650 | 0.864 | Yoshihara A.; Arai I.; Endo A.; Kakiuchi M.; Fujikawa K. | Enhanced Pedestrian Detection Model Transfer-Trained On Yolov8 Using Densefused Rgb And Fir Images | Proceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024 (2024) |
| 28504 | 0.862 | Liu H.; Chen S.; Zheng N.; Wang Y.; Ge J.; Ding K.; Guo Z.; Li W.; Lan J. | Ground Pedestrian And Vehicle Detections Using Imaging Environment Perception Mechanisms And Deep Learning Networks | Electronics (Switzerland), 11, 12 (2022) |
| 43782 | 0.858 | Tahir N.U.A.; Long Z.; Zhang Z.; Asim M.; ELAffendi M. | Pvswin-Yolov8S: Uav-Based Pedestrian And Vehicle Detection For Traffic Management In Smart Cities Using Improved Yolov8 | Drones, 8, 3 (2024) |
| 154 | 0.854 | Lima J.P.; Roberto R.; Figueiredo L.; Simões F.; Thomas D.; Uchiyama H.; Teichrieb V. | 3D Pedestrian Localization Using Multiple Cameras: A Generalizable Approach | Machine Vision and Applications, 33, 4 (2022) |