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One of the most popular approaches to multi-target tracking istracking-by-detection. Current min-cost flow algorithms which solve the dataassociation problem optimally have three main drawbacks: they arecomputationally expensive, they assume that the whole video is given as abatch, and they scale badly in memory and computation with the length of thevideo sequence. In this paper, we address each of these issues, resulting in acomputationally and memory-bounded solution. First, we introduce a dynamicversion of the successive shortest-path algorithm which solves the dataassociation problem optimally while reusing computation, resulting insignificantly faster inference than standard solvers. Second, we address theoptimal solution to the data association problem when dealing with an incomingstream of data (i.e., online setting). Finally, we present our maincontribution which is an approximate online solution with bounded memory andcomputation which is capable of handling videos of arbitrarily length whileperforming tracking in real time. We demonstrate the effectiveness of ouralgorithms on the KITTI and PETS2009 benchmarks and show state-of-the-artperformance, while being significantly faster than existing solvers.

FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation