OCCLUSION-AWARE HMM-BASED TRACKING BY LEARNING

Date

2014-10-30

Author

Marpuc, Tughan
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Recently, an emerging class of methods, namely tracking by detection, achieved quite promising results on challenging tracking data sets. These techniques train a classifier in an online manner to separate the object from its background. These methods only take input location of the object and a random feature pool; then, a classifier bootstraps itself by using the current tracker state and extracted positive and negative samples. Following these approaches, a novel tracking system is proposed. A feature selection method is introduced to increase the discriminative power of the classifier. During tracking, a Hidden Markov Model (HMM) is utilized to filter the features that improve the performance. Moreover, a state of the proposed HMM is allocated to handle occlusions. The proposed tracker is tested on publicly available challenging video sequences and superior tracking results are achieved in real-time.

Subject Keywords

Tracking by detection, Discriminative methods, Hidden Markov models, Occlusion handling

URI

https://hdl.handle.net/11511/55934

Conference Name

IEEE International Conference on Image Processing (ICIP)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

T. Marpuc and A. A. Alatan, “OCCLUSION-AWARE HMM-BASED TRACKING BY LEARNING,” presented at the IEEE International Conference on Image Processing (ICIP), Paris, FRANCE, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55934.