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Part-Based Object Tracking Using Multiple Adaptive Correlation Filters
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DOI：10.1109/TIM.2021.3088455.png)
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摘要
En 中文
Object tracking is challenging, and recently, correlation filters methods have been proposed for this task. Most of these methods focus on the central portion of the target and are negatively affected by changes in the target size and shape. This work proposes a collaborative scheme using several local correlation filters combined with a global correlation filter for improving the performance of object tracking methods based on correlation filters. The proposed correlation filter used in this scheme is based on features extracted from multiple layers of deep convolutional neural networks, and a strategy to identify when these models should also be updated is presented. Experiments show that the proposed scheme tends to be consistent and to achieve better results than other comparative tracking approaches. The proposed collaborative approach can be applied to other correlation filters, which tends to further improve the tracker performance.
Keyword:
Correlation filter
deep neural networks
object detection
object tracking
part-based tracker
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期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
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