StrongSORT: Make DeepSORT Great Again

By Yunhao Du, Zhicheng Zhao, Yang Song, Yanyun Zhao, Fei Su, Tao Gong, Hongying Meng. IEEE transactions on multimedia, 2022

968 citations recorded by the source corpus.

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly, remarkable progresses have been achieved. However, the existing methods tend to use various basic models (e.g, detector and embedding model), and different training or inference tricks, etc. As a result, the construction of a good baseline for a fair comparison is essential. In this paper, a classic tracker, i.e., DeepSORT, is first revisited, and then is significantly improved from multiple perspectives such as object detection, feature embedding, and trajectory association. The proposed tracker, named StrongSORT, contributes a strong and fair baseline for the MOT community. Moreover, two lightweight and plug-and-play algorithms are proposed to address two inherent “missing” problems of MOT: missing association and missing detection. Specifically, unlike most methods, which associate short tracklets into complete trajectories at high computation complexity, we propose an appearance-free link model (AFLink) to perform global association without appearance information, and achieve a good balance between speed and accuracy. Furthermore, we propose a Gaussian-smoothed interpolation (GSI) based on Gaussian process regression to relieve the missing detection. AFLink and GSI can be easily plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). Finally, by fusing StrongSORT with AFLink and GSI, the final tracker (StrongSORT++) achieves state-of-the-art results on multiple public benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/faaf6f4b4f831c50453c72858bc4656269039928

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Citations in the corpus

BoT-SORT: Robust Associations Multi-Pedestrian Tracking (2022, methodology): “Thus, tracking-by-detection trackers mainly focus on improving data association, while exploiting deep learning trends [58, 12].”
Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification (2023, methodology): “In previous work [3, 4], the deep visual embedding used to describe a tracklet is given by an Exponential Moving Average (EMA) of the deep detection embeddings frame by frame.”
Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study (2026, unknown): “…such as BoT-SORT [3] combine motion and appearance cues with camera-motion compensation and an improved Kalman filter state vector, while StrongSORT [4] revisits the DeepSORT baseline and strengthens detection, feature embedding, and trajectory association with lightweight plug-and-play modules.”