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+# CNN-based Person Detection using Infrared Images for Night-time Intrusion Warning Systems
+
+Supplemental material (source code and data) for the **Sensors** journal submission *CNN-based Person Detection using Infrared Images for Night-time Intrusion Warning Systems*.
+
+## Data preparation
+
+To download the *university* dataset, run the following script:
+
+```
+./download_data.sh
+```
+
+To prepare a new dataset, first create a subfolder under the "dataset" folder (e.g. dataset/myfolder).
+Copy the image files to this subfolder using the naming scheme 1.png 2.png 3.png ... etc.
+Next, create a file "params.txt" in the subfolder with a single line containing the values \[xOffset\] \[yOffset\] \[cropSize\].
+This indicates that the area of interest should start at (xOffset,yOffset) and span a window of cropSize x cropSize.
+
+To create ground truth annotations for each image, use the "annotate.py" script.
+Click on relevant segments of the image to label them. Type the "space" character for the next image and the "r" character to refresh.
+The "margin" parameter controls the size of the neighboring region to perform automatic flood fill.
+
+```
+python annotate.py --dataset university --margin 50
+```
+
+## Baselines
+
+Run the baseline algorithms and compute the resulting accuracy.
+"--method" can be one of "threshold", "threshold\_adp", "backSub" or "kmeans"
+Result images will be displayed if the "--viz" flag is used, otherwise only the accuracy is computed.
+Use the "--save_frame" flag to save results from a specific frame to the "results" folder.
+
+```
+python baselines.py --dataset university --method threshold --viz --save_frame 100
+```
+
+## Convolutional Neural Network
+
+DeepLab_V3 CNN: [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1706.05587.pdf).
+
+[Original implementation](https://sthalles.github.io/deep_segmentation_network/).
+
+The first step is to convert the image files and label files into H5 files for use in training.
+If the "--use_history" flag is included, the input channels will have 3 components:
+(i) infrared image intensity
+(ii) difference image for 1 time step
+(iii) output image from running background subtraction 
+Before doing this, make sure to run "baselines.py" with "--method backSub" to save background subtraction results in the "backSub" folder.
+Otherwise, only the first component is used. 
+
+```
+python process_record.py --dataset beach --use_history
+```
+
+Train the network using "train.py" (150 epochs). The trained model will be saved in "dataset/myfolder/model.ckpt".
+
+```
+python train.py --dataset beach --use_history
+```
+
+Test the network and measure the accuracy. The detection threshold (a number between 0 and 1) controls 
+the confidence level above which a pixel will be considered a positive detection.
+Result images will be displayed if the "--viz" flag is used, otherwise only the accuracy is computed.
+
+```
+python test.py --dataset beach --use_history --detection_threshold 0.99 --viz --save_frame 100
+```
+
+## Dependencies
+
+1. numpy
+2. scipy
+3. matplotlib
+4. h5py
+5. tensorflow
+6. [Deeplab V3](https://github.com/sthalles/deeplab_v3)
+
+## Examples
+
+