You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Global optimization algorithms have shown impressive performance in data-association-based multi-object tracking, but handling online data remains a difficult hurdle to overcome. In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking. We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames. More specifically, in the min-cost multi-commodity network flow, the target-specific similarities are online learned to enforce the local consistency for reducing the complexity of the global data association. Meanwhile, the global data association taking multiple video frames into account alleviates irrecoverable errors caused by the local data association between adjacent frames. To ensure the efficiency of online tracking, we give an efficient near-optimal solution to the proposed min-cost multi-commodity flow problem, and provide the empirical proof of its sub-optimality. The comprehensive experiments on real data demonstrate the superior tracking performance of our approach in various challenging situations.

Cited Abstract(s):
[1]: In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame-to-frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset, and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.

[2]: In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously. Our method allows us to overcome the confinements of data association based MOT approaches; where the performance is dependent on the object detection results provided at input level. At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip. The inference of our structured learning is done through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The proposed Lagrangian relaxation optimization finds the high quality solution to the network. During optimization a soft spatial constraint is enforced between the nodes of the graph which helps reducing the ambiguity caused by nearby targets with similar appearance in crowded scenarios. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. Our experiments involve challenging yet distinct datasets and show that our method can achieve results better than the state-of-art.
Recently, multi-commodity flow has been introduced into multi-object tracking in [1] [2] . Ben Shitrit al [1] employed the multi-commodity network to account for different appearance groups which are fixed beforehand. Each appearance group ( , a basketball team) is supposed to be a specific commodity in the network, and solving multi-commodity flow problems is able to distinguish different appearance groups during the optimization process. Dehghan al [2] have focused on integrating object detector learning and multi-object tracking, where the multi-commodity network is used to track a fixed number of objects in a short video batch. Our approach is different from these methods in that we use a multi-commodity network to formulate a hybrid data association strategy to handle online data. Furthermore, a high-quality near-optimal solution to the min-cost multi-commodity flow problem can be achieved by an efficient algorithm, especially when the number of objects (commodities) is relatively large. Thus we do not need to heuristically prune the graph [1] or iteratively relax the hard constraints [2] .