Summary of Searching a Lightweight Network Architecture For Thermal Infrared Pedestrian Tracking, by Wen-jia Tang et al.
Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking
by Wen-Jia Tang, Xiao Liu, Peng Gao, Fei Wang, Ru-Yue Yuan
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative approach to automatically design optimal neural networks for thermal infrared pedestrian tracking (TIR-PT) tasks, which typically require manual effort from human experts. Building upon AlexNet and ResNet architectures, commonly used in image classification and object detection tasks, the authors employ single-bottom and dual-bottom cells as basic search units and incorporate eight operation candidates within the search space to expedite the process. A random channel selection strategy is employed prior to assessing operation candidates. The searched architecture is retrained using joint classification, batch hard triplet, and center loss, resulting in a high-performance network that balances efficiency with accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find the best way for computers to recognize people walking around at night using special cameras that see heat instead of light. Usually, experts have to design these recognition systems from scratch, but this paper shows how computers can do it automatically! They use clever combinations of small building blocks and test different ways of combining them to find the best approach. The result is a new system that’s really good at recognizing people in thermal infrared images while also being efficient with its computer power. |
Keywords
* Artificial intelligence * Classification * Image classification * Object detection * Resnet * Tracking
