20150325-09 王乃岩：Robust Visual Tracking

2015-3-25 15:15| 发布者: 贾伟中科院合肥| 查看: 6487| 评论: 1|来自: VALSE

摘要: 报告嘉宾2：王乃岩（香港科技大学）;主持人：孟德宇 (西安交通大学);报告时间：2015年3月25日晚21:10（北京时间）;报告题目：Transferring Rich Feature Hierarchies for Robust Visual Tracking;文章信息：Transfe ...

报告嘉宾2：王乃岩（香港科技大学）
主持人：孟德宇 (西安交通大学)
报告时间：2015年3月25日晚21:10（北京时间）
报告题目：Transferring Rich Feature Hierarchies for Robust Visual Tracking http://valser.org/webinar/slide/slides/20150325/RVT.pptx
文章信息：Transferring Rich Feature Hierarchies for Robust Visual Tracking, 2015. [arxiv]
报告摘要：Convolutional Neural Networks (CNNs) have demonstrated its great performance in various vision tasks, such as image classification and object detection. However, there are still some areas that are untouched, such as visual tracking. We believe that the biggest bottleneck of applying CNN for visual tracking is lack of training data. The power of CNN usually relies on huge (possible millions) training data, however in visual tracking we only have one labeled sample in the first frame. In this paper, we address this issue by transferring rich feature hierarchies from an offline pretrained CNN into online tracking. In online tracking, the CNN is also finetuned to adapt to the appearance of the tracked target specified in the first frame of the video. We evaluate our proposed tracker on the open benchmark and a non-rigid object tracking dataset. Our tracker demonstrates substantial improvements over the other state-of-the-art trackers.
报告人简介：I am currently the final year PhD candidate in CSE department, HongKong University of Science and Technology. My supervisor is Prof. Dit-Yan Yeung. Before that, I got my BS degree from Zhejiang University, 2011 under the supervision of Prof. Zhihua Zhang. My research interest focuses on applying statistical computational model to real problems in computer vision and data mining. Currently, I mainly work on sparse representation, matrix factorization, deep learning. Especially I am interested in the area of visual tracking, object detection, image classification and recommender system.

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