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About Human Detection and Tracking With Deep Convolutional Neural Networks
This paper proposes a robust framework for human detection and tracking in noisy and occluded environments using deep convolutional neural networks (CNNs) and data augmentation techniques. The framework uses an aggressive data augmentation method to generate complex training images with variations in poses, intensities, blurring, corruption, and noise. A CNN model is trained on the augmented data to learn robust human features. Additionally, a softmax layer is added to improve detection and classification accuracy. Experimental results show the proposed method outperforms state-of-the-art approaches for human detection and tracking in challenging environments.
Detailed Information
| Author: | ['Андрій Шебеко'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 24 |
| Language: | English |
| Format: | |
| Price: | FREE |
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