**6. Conclusions**

In this paper, an SNAC method has been presented to better distinguish objects for MOT. The online learned SNAC can work well in noisy and small sample environments. An incremental learning SNAC algorithm was proposed to generate reliable tracklets. SNAC was also improved to extract an PAN feature that combines appearance and motion for distinguishing tracklets. Tracklet growth was used to compensate for missing detections to improve the association.

Two sub-experiments were designed to evaluate the performance of SNAC and the PAN feature. The experimental results showed that SNAC could extract discriminative features from detection responses and better distinguish them. Meanwhile, in terms of appearance, PAN had a significant improvement in discrimination over SNAC and could better carry out tracklet association. The whole tracking system was evaluated over the 2D MOT 2015 dataset, and the results were compared with the state-of-the-art methods, showing a comparable performance. Experiments showed that this kind of pure online feature extraction solution is suitable for MOT.

Further research includes two aspects. One is combining more useful information to improve the proposed feature extraction method to better distinguish objects for MOT. Another is improving the efficiency of the proposed method to achieve real-time tracking.

**Author Contributions:** Conceptualization, P.L., X.L., and Z.F.; data curation, P.L. and H.L.; formal analysis, P.L. and X.L.; funding acquisition, X.L. and Z.F.; investigation, P.L. and H.L.; methodology, P.L. and X.L.; project administration, X.L. and Z.F.; resources, Z.F.; software, P.L. and H.L.; supervision, X.L. and Z.F.; validation, P.L. and H.L.; visualization, P.L. and H.L.; writing, original draft, P.L.; writing, review and editing, P.L. and X.L.

**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 61671126.

**Acknowledgments:** The authors would like to acknowledge the Multiple Object Tracking Benchmark platform for providing fair comparative experimental data.

**Conflicts of Interest:** The authors declare no conflict of interest.
