Simplification and Optimization of Visual Tracking Convolutional Neural Networks Parameters
- https://doi.org/10.2991/icmmita-16.2016.46How to use a DOI?
- Visual tracking; convolutional neural network; dynamic compression.
Convolutional Neural Network (CNN), which has achieved great success in traditional computer vision tasks such as image classification, object detection, scene segmentation, etc. has also gained great success in fields of visual tracking. However, the algorithm based on CNN is both computation and memory intensive, which makes it hard to deploy it on various systems, especially for embedded system. In this paper, we choose the champion of VOT 2015 challenge which is multi-domain convolutional neural network for visual tracking (MDNet) as our algorithm prototype. We eliminate the zero value and even more non-zero intermediate values in the process of algorithm to reduce both computation and memory resources. While the threshold of non-zero values is not easily determined, we experiment some thresholds and give the result of compression ratio and tracking accuracy to find the relationship between thresholds and tracking accuracy. Finally we achieve some relationship such as nearly 50% compression ratio with 2.3% area-under-the-curve (AUC) loss and analyze the reason of the case in which algorithm is failed.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhiyong Qin AU - Lixin Yu PY - 2017/01 DA - 2017/01 TI - Simplification and Optimization of Visual Tracking Convolutional Neural Networks Parameters BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 253 EP - 259 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-16.2016.46 DO - https://doi.org/10.2991/icmmita-16.2016.46 ID - Qin2017/01 ER -