Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a largescale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.

Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.

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The Probability Hypothesis Density (PHD) filter is an efficient formulation of multi-target state estimation that circumvents the combinatorial explosion of the multitarget posterior by operating on single-target space without maintaining target identities. In this paper, we propose a multi-target tracker based on the PHD filter that provides instantaneous state estimation and delayed decision on data association. For this purpose, we reformulate the PHD recursion in terms of single-target track hypotheses and solve a mincost flow network for trajectory estimation where measurement likelihoods and transition probabilities are based on multitarget state estimates. In this manner, the presented approach combines global data association with efficient multi-target filtering. We evaluate the approach on a publicly available pedestrian tracking dataset to present state estimation and data association capabilities.