  We propose a new long video dataset (called Track Long and Prosper - TLP) and
benchmark for single object tracking. The dataset consists of 50 HD videos from
real world scenarios, encompassing a duration of over 400 minutes (676K
frames), making it more than 20 folds larger in average duration per sequence
and more than 8 folds larger in terms of total covered duration, as compared to
existing generic datasets for visual tracking. The proposed dataset paves a way
to suitably assess long term tracking performance and train better deep
learning architectures (avoiding/reducing augmentation, which may not reflect
real world behaviour). We benchmark the dataset on 17 state of the art trackers
and rank them according to tracking accuracy and run time speeds. We further
present thorough qualitative and quantitative evaluation highlighting the
importance of long term aspect of tracking. Our most interesting observations
are (a) existing short sequence benchmarks fail to bring out the inherent
differences in tracking algorithms which widen up while tracking on long
sequences and (b) the accuracy of trackers abruptly drops on challenging long
sequences, suggesting the potential need of research efforts in the direction
of long-term tracking.
