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One of the challenges in evaluating multi-object video detection, tracking and classification systems is having publicallyavailable data sets with which to compare different systems. However, the measures of performance for tracking andclassification are different. Data sets that are suitable for evaluating tracking systems may not be appropriate forclassification. Tracking video data sets typically only have ground truth track IDs, while classification video data setsonly have ground truth class-label IDs. The former identifies the same object over multiple frames, while the latteridentifies the type of object in individual frames. This paper describes an advancement of the ground truth meta-data forthe DARPA Neovision2 Tower data set to allow both the evaluation of tracking and classification. The ground truth datasets 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 alsocontains the original bounding box coordinates together with new bounding boxes in instances where un-annotatedobjects were present. The unique IDs are maintained during occlusions between multiple objects or when objects re-enterthe field of view. This will provide: a solid foundation for evaluating the performance of multi-object tracking ofdifferent types of objects, a straightforward comparison of tracking system performance using the standard Multi ObjectTracking (MOT) framework, and classification performance using the Neovision2 metrics. These data have been hostedpublically.

A data set for evaluating the performance of multi-class multi-object video tracking