je5349/kitti

Boston Team for the Udacity/Didi Challenge

kitti

Exploratory Findings for the KITTI Vision Benchmark Suite

KITTI Vision Benchmark Suite site: http://www.cvlibs.net/datasets/kitti/eval_object.php

Overview

Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite

Excerpt from the abstract:

Results from state-of-the-art algorithms reveal that methods ranking high on established datasets
such as Middlebury perform below average when being moved outside the laboratory to the real world.
Our [Karlsruhe Institute of Technology] goal is to reduce this bias by providing challenging benchmarks with novel difficulties to
the computer vision community.

Images

There are four sets of images:

• left color images of object data set (12 GB)
• right color images, if you want to use stereo information (12 GB)
• the 3 temporally preceding frames (left color) (36 GB)
• the 3 temporally preceding frames (right color) (36 GB)

Each is comprised of training and testing sets. The training set has 7,480 images, while the testing set has 7,517 images. Because of the amount of disk space these images consume, we are not able to include them all in this repository. They can be downloaded from http://www.cvlibs.net/datasets/kitti/eval_object.php.

Please see image samples here. The image files are in the images directory.

Dimensions

The images appear to have varying dimensions. However, dimensions for a single scene (left, right, 3 previous left, 3 previous right) are consistent. A quick sampling of images provided the following dimensions (in pixels):

• 000189.png: 1224 x 370
• 000274.png: 1242 x 375
• 001603.png: 1241 x 376
• 001822.png: 1238 x 374

Labels

Labels for the training set can be found in the zip_files directory. A sample is provided with an image sample at here.

Bounding Boxes

The bounding boxes are defined by the bbox values from the data. See the images.md writeup for more detail. See 2D_BBox.ipynb for a demonstration.

Performace Metrics

Easy/Moderate/Hard difficulty

Excerpt from the object detection eval page

Difficulties are defined as follows:
- Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
- Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
- Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %
All methods are ranked based on the moderately difficult results.

That said, the moderate difficulty is of interest. Constraints for a record to be considered moderate difficulty:

• The occluded column is 0 or 1.
• The truncated has a max value of 0.3.
• The max value of the bounding box height is 25.0 (px).
  • The bounding box height is the difference between the top and bottom columns.
  • Being pixel values, you'd think these should be integers. Don't be fooled by the decimal values.

An example of some data records with these columns can be found in images.md.

We can use these constraints to ignore certain data records when measuring the performance of the model.

Velodyne Point Clouds

Object Development Kit