Abstract
In this project, we adapt high-performing CNN architectures to differentiate between scenes with and without abandoned luggage. Using frames from two video datasets, we compare the results of training different architectures on each dataset as well as on combining the datasets. We additionally use network visualization techniques to gain insight into what the neural network sees, and the basis of the classification decision. We intend that our results benefit further work in applying CNNs in surveillance and security-related tasks.
Abstract (translated)
在这个项目中，我们采用高性能的CNN架构来区分有和没有废弃行李的场景。使用来自两个视频数据集的帧，我们比较了在每个数据集上训练不同体系结构的结果以及组合数据集的结果。我们还使用网络可视化技术来深入了解神经网络所看到的内容，以及分类决策的基础。我们希望我们的结果有利于在监视和安全相关任务中应用CNN。
URL
https://arxiv.org/abs/1809.02766
