Long-Term Object Tracking Based on Model Updating and Fast Re-Detection

In the complex tracking environments, such as those with target occlusion and illumination change, the existing correlation filtering tracking algorithm can not stably track the target for long periods in real time. A long-term tracking algorithm based on model update and fast re-detection is proposed. The proposed algorithm initially builds a long-term target tracking framework based on the existing correlation filtering tracking algorithm with target location and the scale change and subsequently proposes a model monitoring updating mechanism, which enter into updating or re-detection link according to the correlation energy values of maximum response and average peak response. Then, the bit dimension of the extracted features can be reduced to 512 using the re-detection method based on extracting descriptor features to accelerate the re-detection rate. The algorithm presented in this study selects 20 representative sequences from OTB-100 dataset for testing. The average success rate, average accuracy, and average rate arc observed to be 0.706, 0.805, and 18.5 frame/s, respectively. Furthermore, the average accuracy of the self-collected datasets reaches 87.65%, satisfying the accuracy and real-time demands of long-term tracking in complex situations, including scale change and occlusion.