To help facilitate faster data processing a down-sampled version of each file is also provided. The labels are provided (“ train_labels.csv” & “ test_labels.csv”) as the distance to the center of gravity of the animal at specific timestamps in each video. Instructions for accessing this can be found on the competition Data tab.Įach video is given a unique 4 letter name and is either. The dataset consists of over 3,500 videos (nearly 200GB of data) split into a training and a test set. The videos are in both color and grayscale for night vision and contain a range of different animals as subjects. The objective of this challenge is to estimate the distance to animals in monocular trail camera footage. He will also throw some tips and tricks in between on how you can improve this code and improve your score.Ĭheckout the links below to sign up for the challenge, receive your complimentary MATLAB Licenses, and visit the discussion forum for support. ![]() Here he will talk about the optical Flow + CNN aproach he used for solving the problem. ![]() James Drummond, from MathWorks Engineering Development group, took a stab at this challenge and prepared this starter code. As promised in our last blog, here we are with the MATLAB benchmark code for the Deep Chimpact: Depth Estimation for Wildlife Conservation Challenge.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |