Journal
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON VIRTUAL REALITY (ICVR 2018)
Volume -, Issue -, Pages 44-49Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3198910.3198918
Keywords
Object tracking; Convolutional neural network; Relative model; Image fusion
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Funding
- National Program on Key Basic Research Project [2014CB744903]
- National Natural Science Foundation of China [61673270]
- Shanghai Pujiang Program [16PJD028]
- Aerospace Science and Technology Innovation Foundation [HTKJCX2015CAAA 09]
- Shanghai Science and Technology Committee Research Project [17DZ1204304]
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In this paper, a novel relative object tracking algorithm using a convolutional neural network is proposed aiming to boost the tracking performance. A two-layer convolutional neural network extracts sparse feature representation of visible and infrared sequences via convolutional filters. The convolutional filters contain two types, object filter, and relative filters. In the first frame, we employ a set of normalized fusion patches as the object filters. Moreover, a relative model is explored to generate relative filters using k-means algorithms, which integrates information from both foreground and background to build accurate appearance model. This algorithm without training is robust and efficient. Quantitative and qualitative evaluations demonstrate that the performance of this algorithm improves significantly over the state-of-the-art techniques when applied to public testing sequences.
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