Discriminative Correlation Filter with Channel and Spatial Reliability
| Autor: | Matej Kristan, Luka Čehovin Zajc, Jiri Matas, Tomas Vojir, Alan Lukezic |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Channel (digital image) business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 020207 software engineering Pattern recognition 02 engineering and technology Object detection Channel reliability Discriminative model Filter (video) Video tracking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Reliability (statistics) |
| Zdroj: | CVPR |
| Popis: | Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard features, HoGs and Colornames, the novel CSR-DCF method -- DCF with Channel and Spatial Reliability -- achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs in real-time on a CPU. Accepted to: International Journal of Computer Vision: https://link.springer.com/article/10.1007/s11263-017-1061-3 |
| Databáze: | OpenAIRE |
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