Neural Network Control for Active Cameras Using Master-Slave Setup

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Source code associated with the paper Neural network control for active cameras using master-slave setup, published in AVSS 2018. The package includes the code for a learning-based approach to control the master-slave setup and a framework to compare different methods for the master-slave camera system. Neural network control for active cameras using master-slave setup

Prerequisites:
Python 3.6
Numpy 1.4
Opencv 3.4
Keras 2.1.5
Tensorflow-gpu 1.7

Download the Yolo model and weight and move them to folder “model_data”

How to run:
Step one: Open the file “01-training_corresponding_points.py” and set camera ip. Run the file and the information will be saved in the folder “AutoMS”. To create the corresponding points follow the steps defined in our paper.

Step two: Open the file “02-training_mlp_model.py”, set the path of the corresponding points text file and run the python file to train the model.

Step three: Open the file “03-Reis_et_al._method_versus_your_method.py” you should set the cameras’ ip and the path of the neural network model/weight.

Contact: Renan Reis (renanreis1@gmail.com)

References

You should cite the following paper if you use this software in your work.

Renan Oliveira Reis; Igor Henrique Dias; William Robson Schwartz: Neural network control for active cameras using master-slave setup. In: International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2018. (Type: Inproceedings | Links | BibTeX)

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