StrongSORT: Make DeepSORT Great Again

Abstract

Multi-Object Tracking (MOT) has gained lots attention from researchers and achieved remarkable progress in recent years. However, recent studies on MOT tend to use different basic models (e.g, detector and embedding model), training data and training/inference tricks, which makes it difficult to construct a fair comparison between their progress. In this paper, we revisit the classic tracker DeepSORT, and upgrades it from aspects of detection, embedding, and association. The proposed tracker, named StrongSORT, achieves great improvements over DeepSORT, and can serve as a strong and fair baseline for other methods. We also present two lightweight and plugand-play algorithms to solve two "missing" problems in MOT. Firstly, to solve the missing association problem, some works associate short tracklets into complete trajectories with computationally expensive models. Instead, we propose the appearancefree link model (AFLink) to perform global association without appearance information, which achieves a better trade-off between speed and accuracy. Secondly, for the missing detection problem, we propose Gaussian-smoothed interpolation (GSI), which improves the linear interpolation algorithm with Gaussian process regression. Moreover, AFLink and GSI can be plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). By integrating StrongSORT with the two algorithms, the final tracker StrongSORT++ achieves SOTA results on multiple benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking.

Helmet-Wearing Tracking Detection Based on StrongSORT

“…At the same time, DeepSORT introduces cascade matching and new trajectory confirmation to improve the optimal matching of the predicted trajectory with the object in the current frame. Du, Y. et al [ 28 ] proposed StrongSORT; two plug-and-play lightweight algorithms are introduced: AFLink and GSI. Among them, the AFLink model associates the short trajectory as a complete trajectory, which is a fully connected model without appearance information, GSI improves the absence in detection by simulating nonlinear motion, achieves more accurate positioning based on Gaussian regression, and does not ignore the motion information of the detected object during the regression process.…”

“…In the AFLink model, trajectories and are used, as shown in Figure 2 for the AFLink model structure, where consists of the position information of the last 30 frames and the frame . and will be input into the time module and the fusion module [ 28 ]. The time module is used to extract frame feature information, and then the fusion module is used to perform feature fusion on the extracted frames of different dimensions, and then the classifier predicts the correlation between the two frames.…”

“…In addition, the use of DeepSORT [ 27 ] and StrongSORT [ 28 ] algorithms are selected in this paper. The above algorithms are combined with the YOLOv5s + Focal-EIOU detector for comparative experiments.…”

“…Based on the original SORT [ 26 ] algorithm, they proposed to combine the motion and appearance information of the target to track. In 2022, the StrongSORT [ 28 ] tracking algorithm was proposed, and two lightweight plug-and-play algorithms, AFLink model and Gaussian Smoothed Interpolation, were proposed. The feature library and cascade matching in DeepSORT algorithm were replaced by [ 29 ] feature extraction and vanilla global linear matching strategy.…”

“…Combined with the above problems, this paper takes the construction personnel wearing helmets as the monitoring object, and processes more than 8000 images as the training dataset. YOLOv5 is selected as the detector for training, and the StrongSORT [ 28 ] tracking algorithm with good performance is used to achieve target tracking, so as to achieve the monitoring requirement of wearing a safety helmet under the construction background.…”