Synthetic Track Prediction

1. Generating and Plotting Synthetic Tracks

We'll begin by generating coordinates (x and y) to create synthetic track lines. Specifically, we'll generate two classes of tracks.

1.1 Memory Concept

Each generated track shares a memory factor. This memory factor indicates the degree of similarity between tracks. For instance, tracks with a memory factor of 0.9 exhibit 90% similarity.

1.2 Normalization

We'll apply a normalization formula to ensure that the generated tracks fall within the range of 0 to 1.

1.3 Matplotlib plot

We'll plot tracks with Matplotlib.

1.4 Opencv plot

• We will use OpenCV to plot tracks, both with aliasing and antialiasing.
• The tracks will be shifted to the center of the image before plotting.

2. Classification

In this phase, we'll utilize a CNN model for classification.

2.1 Training

For training, the following options are available for the model:

-d <Name of dataset to use in the experiment>
-n <Give name to experiment>
-i <Image size> [default=112]
-l <Learning rate> [default=0.001]
-e <Num of epochs> [default=30]
-b <Base directory> [CHANGE WITH DIRECTORY OF SYSTEM]

Example usage: -d aliased_tracks_112_l1 -n exp_1 -e 20

2.2 Evaluation

In the evaluation phase, we will:

• Count errors in prediction and calculate the accuracy.
• Calculate the confusion matrix.

Before running evaluation, ensure to set the dataset and exp_name according to the experiment you want to evaluate:

dataset = 'aliased_tracks_112_l1'
exp_name = 'exp_1'