StrongSORT - 

0) Motivation, Object and Related works:

Motivation:

Objectives:

• proposed two lightweight plug-and-play post-processing algorithms: an appearance-free link model (AFLink) and Gaussian-smoothed interpolation (GSI), to refine the tracking results

• Compared to DeepSORT, StrongSort upgrades the embedding by replacing CNN with BoT with ResNeSt50 backbone and pre-trained on the DukeMTMCreID dataset to extract more discriminative features. In addition, they replaced the feature bank with an exponential moving average (EMA). For the motion branch, they adopt Enhanced Correlation Coefficient (ECC) for camera motion compensation and replace vanilla Kalman filter with NSA Kalman algorithm. Furthermore, during feature matching, they solve the assignment problem with both appearance and motion information instead of employing only the appearance feature distance. Lastly, they replace the matching cascade with a vanilla global linear assignment.

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• Besides upgrading from DeepSORT, they propose two post-processing algorithms, AFLink and GSI. AFLink predicts the connectivity between two tracklets by relying only on spatio-temporal information and GSI interpolates tracks with Gaussian process regression. StrongSORT with AFLink and GSI achieves state-of-the-art performance on MOT17 and MOT20 datasets. Moreover, AFLink and GSI can be also applied to other tracking algorithms and it improved the tracking performance.

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References:

• https://sertiscorp.medium.com/multi-object-tracking-a-review-6aaeea495209

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