• Introduction
• Challenge
  • Data extraction
• Methods and solution
  • 0. Image pre-processing
  • 1. Color channels approach
  • 2. Autoencoder approach
    • Initial idea
    • Model Architecture
    • Implementation and findings

This is part of the exercise class "UE Computer Vision, Oliver Bimber / Indrajit Kurmi, 2021W" at the JKU Austria.

• The responsible institute at JKU is https://www.jku.at/en/institute-of-computer-graphics/
• Whereas they have a specific research project for Search and rescue with airborne optical sectioning
• For further projects see: https://www.jku.at/en/institute-of-computer-graphics/research/projects/2021

In this lab project, we had to implement an unsupervised person localization algorithm.

• OpenCV functions cv2.findContours() and cv2.boundingRect()
• Pad by 2 pixels in each direction
• Merge overlapping bounding boxes
• Pad by 7 pixels in x and 4 pixels in y
• Choose the biggest bounding box of blue and red image, respectively
• Remove if x<24 or y<18
• If no detections -> lower threshold for binary image and start from 1
• Merge detections overlapping between the two images
• Pad detections to a minimum size of 38x30 px

• Advantage:
  • can distinguish people from other objects by detecting movement
• Disadvantages:
  • bias towards detecting people wearing blue or red – problems finding people with green clothing
  • cannot detect people that are not moving or moving too little

The autoeconder is implemented in anomaly_detection_autoencoder_SAR_JKU.ipynb

Going through various research papers on anomaly detection, we decided to try out an Autoencoder approach for this task

• Autoencoder -> encoder-decoder system to reconstruct the input as the output.

• Train a convolutional autoencoder so that it will reconstruct an image from the normal data with a smaller reconstruction error, but reconstruct an image from the anomaly data with a larger reconstruction error

• Our solution decides if an image is from the normal data or from the anomaly data based on a threshold of the reconstruction error.

• the model is encouraged to learn to precisely reproduce the most frequently observed characteristics

• when facing anomalies, the model should worsen its reconstruction performance.

• after training, the autoencoder will accurately reconstruct normal data, while failing to do so with unfamiliar anomalous data

• reconstruction error (the error between the original data and its low dimensional reconstruction) is used as an anomaly score to detect anomalies

• we are aware that autoencoding models can, be very good at reconstructing anomalous examples and consequently not able to reliably perform anomaly detection

• Base is a Convolutional autoencoder for image denoising from official Keras docs
• Adapted loss for Structural Similarity Index (SSIM)
• Decided for that because
  • Relatively straight forward to tune
  • Simple architecture
  • Sufficient for our image detection problem

Over the course of the implementation it became apparent, that

• properly pre-processed images improve the performance of the autoencoder a lot
• a deep convolutional autoencoder is sufficient to reproduce the images properly
• the autoencoder should be trained with color images as the color provides most of the information for the task
• the biggest challenge is the length of training as
  • too short training shows too many reconstruction errors
  • too long training reconstructs anomalies
• as well as the threshold for finding the most useful SSIM differences

Reconstruction worked well

Visualization of activation layers over RBG channels, showing stronger activations for red and blue channel

Indication of finding the anomalies as desired.

Finding the proper threshold for SSIM differences

The project was implemented over the course of a semester at university.

In the end we implemented the whole pipeline to fit the corresponding grading criteria.