A Hybrid Approach To Real-Time Multi-Object Tracking
Vincenzo Mariano Scarrica Ciro Panariello Alessio Ferone Antonino Staiano
Multi-Object Tracking, also known as Multi-Target Tracking, is a significant
area of computer vision that has many uses in a variety of settings. The
development of deep learning, which has encouraged researchers to propose more
and more work in this direction, has significantly impacted the scientific
advancement around the study of tracking as well as many other domains related
to computer vision. In fact, all of the solutions that are currently
state-of-the-art in the literature and in the tracking industry, are built on
top of deep learning methodologies that produce exceptionally good results.
Deep learning is enabled thanks to the ever more powerful technology
researchers can use to handle the significant computational resources demanded
by these models. However, when real-time is a main requirement, developing a
tracking system without being constrained by expensive hardware support with
enormous computational resources is necessary to widen tracking applications in
real-world contexts. To this end, a compromise is to combine powerful deep
strategies with more traditional approaches to favor considerably lower
processing solutions at the cost of less accurate tracking results even though
suitable for real-time domains. Indeed, the present work goes in that
direction, proposing a hybrid strategy for real-time multi-target tracking that
combines effectively a classical optical flow algorithm with a deep learning
architecture, targeted to a human-crowd tracking system exhibiting a desirable
trade-off between performance in tracking precision and computational costs.
The developed architecture was experimented with different settings, and
yielded a MOTA of 0.608 out of the compared state-of-the-art 0.549 results, and
about half the running time when introducing the optical flow phase, achieving
almost the same performance in terms of accuracy.