Correlation filter tracking algorithms against interference of similar object and fast motion

fDSST (fast Discriminative Scale Space Tracking) belongs to correlation filter tracking algorithm, which has high success rate and precision, also runs at a fast speed. However, it is still a huge challenge for the tracking scene of fast motion and similar object interference. In order to improve the performance of fDSST on the challenges above, this paper proposed fDSSTs algorithm and fDSSTss algorithm respectively. fDSSTs increases the response scores near the object location by fusing the fhog feature and the color statistical feature, so improved the tracking performance of fDSST in the fast moving scene. fDSSTss adds a multi-feature object association module on the basis of fDSST, which distinguishes the real object and the interference object from the object feature level, thereby maintaining the tracking of the real object. The fDSSTs is tested on the OTB50 dataset, in fast-moving scenarios, the success rate of fDSST is improved by 20.5% and the precision is improved by 22.8% compared with fDSST. The fDSSTss is tested on the test sequences of similar object interference, and the result shows that fDSSTss has better anti-similar object interference ability than fDSST, while meeting the real-time requirements. The experiments show that the improvements improve the success rate and precision of fDSST in fast object moving scenes, as well as the ability to resist similar object interference.