Back to Search
Start Over
Pedestrian Fall Event Detection in Complex Scenes Based on Attention-Guided Neural Network.
- Source :
- Mathematical Problems in Engineering; 4/28/2022, p1-10, 10p
- Publication Year :
- 2022
-
Abstract
- To address automatic detection of pedestrian fall events and provide feedback in emergency situations, this paper proposes an attention-guided real-time and robust method for pedestrian detection in complex scenes. First, the YOLOv3 network is used to effectively detect pedestrians in the videos. Then, an improved DeepSort algorithm is used to track by detection. After tracking, the authors extract effective features from the tracked bounding box, use the output of the last convolutional layer, and introduce the attention weight factor into the tracking module for final fall event prediction. Finally, the authors use the sliding window for storing feature maps and SVM classifier to redetect fall events. The experimental results on the CityPersons dataset, Montreal fall dataset, and self-built dataset indicate that this approach has good performance in complex scenes. The pedestrian detection rate is 87.05%, the accuracy of fall event detection reaches 98.55%, and the delay is within 120 ms. [ABSTRACT FROM AUTHOR]
- Subjects :
- PEDESTRIANS
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 1024123X
- Database :
- Complementary Index
- Journal :
- Mathematical Problems in Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 156584985
- Full Text :
- https://doi.org/10.1155/2022/4110246