  Annotating a large-scale in-the-wild person re-identification dataset
especially of marathon runners is a challenging task. The variations in the
scenarios such as camera viewpoints, resolution, occlusion, and illumination
make the problem non-trivial. Manually annotating bounding boxes in such
large-scale datasets is cost-inefficient. Additionally, due to crowdedness and
occlusion in the videos, aligning the identity of runners across multiple
disjoint cameras is a challenge. We collected a novel large-scale in-the-wild
video dataset of marathon runners. The dataset consists of hours of recording
of thousands of runners captured using 42 hand-held smartphone cameras and
covering real-world scenarios. Due to the presence of crowdedness and occlusion
in the videos, the annotation of runners becomes a challenging task. We propose
a new scheme for tackling the challenges in the annotation of such large
dataset. Our technique reduces the overall cost of annotation in terms of time
as well as budget. We demonstrate performing fps analysis to reduce the effort
and time of annotation. We investigate several annotation methods for
efficiently generating tight bounding boxes. Our results prove that
interpolating bounding boxes between keyframes is the most efficient method of
bounding box generation amongst several other methods and is 3x times faster
than the naive baseline method. We introduce a novel way of aligning the
identity of runners in disjoint cameras. Our inter-camera alignment tool
integrated with the state-of-the-art person re-id system proves to be
sufficient and effective in the alignment of the runners across multiple
cameras with non-overlapping views. Our proposed framework of annotation
reduces the annotation cost of the dataset by a factor of 16x, also effectively
aligning 93.64% of the runners in the cross-camera setting.
