  One of the challenges in evaluating multi-object video detection, tracking
and classification systems is having publically available data sets with which
to compare different systems. However, the measures of performance for tracking
and classification are different. Data sets that are suitable for evaluating
tracking systems may not be appropriate for classification. Tracking video data
sets typically only have ground truth track IDs, while classification video
data sets only have ground truth class-label IDs. The former identifies the
same object over multiple frames, while the latter identifies the type of
object in individual frames. This paper describes an advancement of the ground
truth meta-data for the DARPA Neovision2 Tower data set to allow both the
evaluation of tracking and classification. The ground truth data sets presented
in this paper contain unique object IDs across 5 different classes of object
(Car, Bus, Truck, Person, Cyclist) for 24 videos of 871 image frames each. In
addition to the object IDs and class labels, the ground truth data also
contains the original bounding box coordinates together with new bounding boxes
in instances where un-annotated objects were present. The unique IDs are
maintained during occlusions between multiple objects or when objects re-enter
the field of view. This will provide: a solid foundation for evaluating the
performance of multi-object tracking of different types of objects, a
straightforward comparison of tracking system performance using the standard
Multi Object Tracking (MOT) framework, and classification performance using the
Neovision2 metrics. These data have been hosted publically.
