https://github.com/atcold/smarteye
Implementation of the VADNN article
https://github.com/atcold/smarteye
Last synced: 8 months ago
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Implementation of the VADNN article
- Host: GitHub
- URL: https://github.com/atcold/smarteye
- Owner: Atcold
- Created: 2015-06-15T20:42:38.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2015-06-15T22:09:32.000Z (almost 11 years ago)
- Last Synced: 2025-05-29T15:15:45.870Z (11 months ago)
- Language: Lua
- Size: 118 KB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# smartEYE
This is an implemetation of the [VADNN](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7086900) article.
This collection of scripts for [Torch7](http://torch.ch/) can compute both *bottom-up* and *top-down* visual attention saliency maps.
## You need to fetch some data
You need to fetch the trained model, at least, to be able to run the live demo.
```bash
./getData-public.sh
```
## Dependency
If you want to use an USB camera, you will need to install the `camera` package
```bash
luarocks install camera
```
## How to run a live person top-down saliency demo
There are available three different methods with which the final top-down saliency map is displayed.
Each method can be selected by `--mode #`, where `#` can be 1, 2 or 3.
The source can be a USB webcam, which is the default option, or an Ethernet camera, which can be chosen with `--eth`.
For example, we can run on USB, mode 3 by typing
```bash
cd dev
qlua saliencyCam.lua --mode 3
```
or, we could run on Ethernet and using method 2 with
```bash
qlua saliencyCam.lua --mode 2 --eth
```