Compressed multi-block local binary pattern for object tracking

Tianwen Li; Yun Gao; Lei Zhao; Hao Zhou

doi:10.1117/12.2310115

13 April 2018 Compressed multi-block local binary pattern for object tracking

Tianwen Li, Yun Gao, Lei Zhao, Hao Zhou

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 1069609 (2018) https://doi.org/10.1117/12.2310115
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.

Citation Download Citation

Tianwen Li, Yun Gao, Lei Zhao, and Hao Zhou "Compressed multi-block local binary pattern for object tracking", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069609 (13 April 2018); https://doi.org/10.1117/12.2310115

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