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- W2898022204 abstract "Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work." @default.
- W2898022204 created "2018-10-26" @default.
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- W2898022204 date "2018-07-01" @default.
- W2898022204 modified "2023-09-25" @default.
- W2898022204 title "A Comparative Study of Object Tracking using CNN and SDAE" @default.
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- W2898022204 doi "https://doi.org/10.1109/ijcnn.2018.8489742" @default.
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