A Deep Learning Approach for Pedestrian Behavior Interpretation Based on Radar Point Clouds

Sezgin, Fatih; Vriesman, Daniel; Held, Patrick; Zimmer, Alessandro; Brandmeier, Thomas

doi:10.23919/eurad50154.2022.9784515

Cited by 4 publications

(1 citation statement)

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“…Such approaches are adequate for simple cases. However, the performance is not satisfactory, and the efficacy is low [6]. With the fast growth of DL, particularly the proposal of generic object recognition, DL-related techniques for pedestrian recognition attain substantial enhancements in the context of accuracy and speed [7].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Gogineni

Bhuyan

Lydia³

2023

Preprint

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Pedestrian detection using object detection and deep learning has been found to be effective method for identifying pedestrians in video frames or images accurately. It is more commonly used in many real-time applications, such as security observing systems, autonomous driving systems, and robotics. The combination of deep learning techniques and object detection algorithms allows efficient and robust detection of pedestrians in several real-time scenarios. But it is necessary to improve the detection efficacy for complex environments such as cases with worse visibility due to weather or daytime, crowd scenes, and rare pose samples. Continuous improvement and research in DL algorithms, dataset collection, and training models contribute to accelerating the robustness and accuracy of pedestrian detection systems. Therefore, this study designs a new marine predator algorithm with deep learning-based pedestrian detection and classification (MPADLB-PDC) technique. The objective of the MPADLB-PDC approach lies in the accurate recognition and classification of pedestrians. To achieve this, the MPADLB-PDC technique involves two major processes, namely object detection and classification. In the first stage, the MPADLBPDC technique uses an improved YOLOv7 object detector for the recognition of the objects in the frame. Next, in the second stage, the ensemble classifier comprises three classifiers such as deep feed-forward neural network (DFFNN), extreme learning machine (ELM), and long short-term memory (LSTM). To improve the recognition performance of the ensemble classifier, the MPA is used to optimally select the parameters related to it. The simulation outcome of the MPADLB-PDC system was validated on the pedestrian database, and the outcome can be studied interms of various aspects. The simulation values validated the better outcome of the MPADLB-PDC system compared to other approaches.

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Section: Introductionmentioning

confidence: 99%