A method for tracking goods carriers from a particular source, the method comprising: receiving a plurality of training image files, wherein each of the plurality of training image files comprises a respective training image that depicts a respective plurality of goods carriers from the particular source; for each of the plurality of training image files: using an artificial neural network to determine an estimated number of the respective plurality of goods carriers from the particular source in the respective training image; and determining a comparison of the estimated number of the respective plurality of goods carriers from the particular source in the respective training image with a known number of the respective plurality of goods carriers from the particular source in the respective training image; using the comparisons to train the artificial neural network by adjusting weights associated with artificial neurons within the artificial neural network; receiving a first image file generated by a first imaging device, wherein the first image file comprises a first image that depicts a first plurality of goods carriers from the particular source, and the first image is associated with a transportation path having a plurality of nodes; using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image; receiving a second image file generated by a second imaging device, wherein the second image file comprises a second image that depicts a second plurality of goods carriers from the particular source, and the second imaging device being located in the transportation path; using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image; and determining whether the first number of goods carriers from the particular source in the first image is equal to the second number of goods carriers from the particular source in the second image. receiving a plurality of training image files, wherein each of the plurality of training image files comprises a respective training image that depicts a respective plurality of goods carriers from the particular source; for each of the plurality of training image files: using an artificial neural network to determine an estimated number of the respective plurality of goods carriers from the particular source in the respective training image; and determining a comparison of the estimated number of the respective plurality of goods carriers from the particular source in the respective training image with a known number of the respective plurality of goods carriers from the particular source in the respective training image; using an artificial neural network to determine an estimated number of the respective plurality of goods carriers from the particular source in the respective training image; and determining a comparison of the estimated number of the respective plurality of goods carriers from the particular source in the respective training image with a known number of the respective plurality of goods carriers from the particular source in the respective training image; using the comparisons to train the artificial neural network by adjusting weights associated with artificial neurons within the artificial neural network; receiving a first image file generated by a first imaging device, wherein the first image file comprises a first image that depicts a first plurality of goods carriers from the particular source, and the first image is associated with a transportation path having a plurality of nodes; using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image; receiving a second image file generated by a second imaging device, wherein the second image file comprises a second image that depicts a second plurality of goods carriers from the particular source, and the second imaging device being located in the transportation path; using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image; and determining whether the first number of goods carriers from the particular source in the first image is equal to the second number of goods carriers from the particular source in the second image.