Tracking in streamed video by updating globally optimal matchings

Matching methods such as the Hungarian algorithm have recently made an appearance as an alternative to classical tracking algorithms in computer vision, since they are able to find the set of tracks that optimizes well-defined criteria over a given video sequence. However, despite being globally optimal, they carry a cost: since they require complete knowledge of the sequence, such methods cannot work with continuous video streams, a crucial requirement of realistic video surveillance applications. We were able to use the recently proposed Dynamic Hungarian Algorithm in an innovative way, adapting it to the well-known sliding window methodology. The algorithm is able to run in real-time, while retaining its optimality. We tested our implementation on several datasets, tracking humans and vehicles, and obtained reliable results using the same set of parameters on all sequences.