  The problem of tracking multiple objects in a video sequence poses several
challenging tasks. For tracking-by-detection, these include object
re-identification, motion prediction and dealing with occlusions. We present a
tracker (without bells and whistles) that accomplishes tracking without
specifically targeting any of these tasks, in particular, we perform no
training or optimization on tracking data. To this end, we exploit the bounding
box regression of an object detector to predict the position of an object in
the next frame, thereby converting a detector into a Tracktor. We demonstrate
the potential of Tracktor and provide a new state-of-the-art on three
multi-object tracking benchmarks by extending it with a straightforward
re-identification and camera motion compensation. We then perform an analysis
on the performance and failure cases of several state-of-the-art tracking
methods in comparison to our Tracktor. Surprisingly, none of the dedicated
tracking methods are considerably better in dealing with complex tracking
scenarios, namely, small and occluded objects or missing detections. However,
our approach tackles most of the easy tracking scenarios. Therefore, we
motivate our approach as a new tracking paradigm and point out promising future
research directions. Overall, Tracktor yields superior tracking performance
than any current tracking method and our analysis exposes remaining and
unsolved tracking challenges to inspire future research directions.
