Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking

Chao Ma Jia-Bin Huang Xiaokang Yang Ming-Hsuan Yang

Shanghai Jiao Tong University Virginia Tech UC Merced

1. Abstract

Object tracking is challenging as target objects often undergo drastic appearance changes over time. Recently, adaptive correlation filters have been successfully applied to object tracking as these filters with short-term memory are robust to large appearance changes. However, tracking algorithms relying on highly adaptive correlation filters are prone to drift due to noisy updates. Moreover, as these algorithms do not maintain long-term memory of target appearance, they cannot recover from tracking failures caused by heavy occlusion or target objects moving out of the view. In this paper, we propose to learn multiple adaptive correlation filters with both long-term memory and short-term memory of target appearance for robust object tracking. First, we learn a kernelized correlation filter with an aggressive learning rate for locating target objects precisely. We take into account the appropriate size of surrounding context and the feature representations. Second, we learn a correlation filter over a feature pyramid centered at the estimated target position for predicting scale changes. Third, we learn a complementary correlation filter with a conservative learning rate to maintain long-term memory of target appearance. We use the responses of correlation filters to determine if tracking failure occurs. In case of tracking failure, we apply an incrementally learned detector to recover the target position in a sliding window fashion. Extensive experimental results on large-scale benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness.

2. Publications and Downloads

Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking

Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang

Internation Journal of Computer Vision (IJCV), 2018

[Manuscript] [Matlab Code] [Video Results] [Our results on OTB 2013 & 2015] [Our deep results on OTB 2013 & 2015]

Results of all trackers on OTB 2015 [GoogleDrive] [BaiduYun]

Long-Term Correlation Tracking

Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

[Paper] [Abstract] [Supplementary] [Matlab Code] [Results on OTB2013]

3. Experimental Results

Overall Performance on the OTB-2013 dataset

Overall Performance on the OTB-2015 dataset

4. References

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Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3), 583–596 (2015)
Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015)
Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2012)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: A benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)
Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 7(9), 1834–1848 (2015)
Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of British Machine Vision Conference (2014)
Zhang, J., Ma, S., Sclaroff, S.: MEEM: Robust tracking via multiple experts using entropy minimization. In: Proceedings of the European Conference on Computer Vision (2014)

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