Using near-duplicate video frames to analyze, classify, track, and visualize evolution and fitness of videos

• 1. 从广播视频生成可跟踪视频帧的系统及方法 [P] . 中国专利： CN113508604A . 2021-10-15
• 2. 一种视频帧前景跟踪方法以及视频编码器 [P] . 中国专利： CN101877786B . 2012.08.15
• 3. Using near-duplicate video frames to analyze, classify, track, and visualize evolution and fitness of videos [P] . 美国专利： US8798402B2 . 2014-08-05

  机译： 使用近乎重复的视频帧来分析，分类，跟踪和可视化视频的演化和适应性

• 4. USING NEAR-DUPLICATE VIDEO FRAMES TO ANALYZE, CLASSIFY, TRACK, AND VISUALIZE EVOLUTION AND FITNESS OF VIDEOS [P] . 美国专利： US2012321201A1 . 2012-12-20

  机译： 使用近乎重复的视频帧来分析，分类，跟踪和可视化视频的演化和适合性

• 5. USING NEAR-DUPLICATE VIDEO FRAMES TO ANALYZE, CLASSIFY, TRACK, AND VISUALIZE EVOLUTION AND FITNESS OF VIDEOS [P] . 美国专利： US2012099785A1 . 2012-04-26

  机译： 使用近乎重复的视频帧来分析，分类，跟踪和可视化视频的演变和适合性

• 1. Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos [J] . Xiaohui Yuan, Longbo Kong, Dengchao Feng, 自动化学报：英文版 . 2017,第004期
• 2. Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos [J] . Xiaohui Yuan, Longbo Kong, Dengchao Feng, 自动化学报（英文版） . 2017,第004期
• 3. A new approach for real time object detection and tracking on high resolution and multi-camera surveillance videos using GPU [J] . Mohammad Farukh Hashmi, Ritu Pal, Rajat Saxena, 中南大学学报（英文版） . 2016,第001期
• 4. Analyzing animal behavior via classifying each video frame using convolutional neural networks [J] . Ulrich Stern, Ruo He, Chung-Hui Yang Scientific reports. . 2015,第期
• 5. Pattern-Based Near-Duplicate Video Retrieval and Localization on Web-Scale Videos [J] . Chien-Li Chou, Hua-Tsung Chen, Suh-Yin Lee Multimedia, IEEE Transactions on . 2015,第3期
• 6. Cross-frame keypoint-based and spatial motion information-guided networks for moving vehicle detection and tracking in satellite videos [J] . Feng Jie, Zeng Dening, Jia Xiuping, ISPRS Journal of Photogrammetry and Remote Sensing . 2021,第Jula期
• 7. Large-Scale News Topic Tracking and Key-scene Ranking with Video Near-Duplicate Constraints [C] . Xiaomeng Wu, rnIchiro Ide, rnShinichi Satoh 1st ACM workshop on large-scale multimedia retrieval and mining 2009 . 2009
• 8. Tracking epileptic patients in digital videos for automated video-EEG monitoring. [D] . Singh, Pramit. 2007
• 9. Analyzing animal behavior via classifying each video frame using convolutional neural networks [O] . Ulrich Stern, Ruo He, Chung-Hui Yang -1
• 10. Occupational therapy intervention with a child is based upon an understanding and appreciation of normal development. Knowledge of current concepts and theories related to child development is essential when occupational therapist evaluates children. This background information helps therapist to plan intervention for the child. The aim of this study is to make observation video about development of about one year old child. The purpose of my study is to help occupational therapy students learn about child development. My study is practice-based thesis. It includes product, which is the observation video and study rapport. I describe my whole process in my rapport. The process includes different kinds of stages. First, I studied those theories of child development, which are used in the studies of occupational therapy for children. These theories are Moseys Developmental Frame of Reference and the theory of development according to Sensory Integration Theory. These theories are the frames of reference of my study. I organize the child development areas according to child occupations and skills. Then I start to plan, film and edit my video based on the theories of child development and the principles of making a video. In my rapport I describe all the stages of my study and explain the sequence and the content of the stages. I also evaluate the process of my study. In the observation video you can see those stages of development where about one year old child is based on the frames of reference, which I have used in my study. I believe that my observation video can at least be good for inspiring occupational therapy students learning about child development. Keywords child development, learning, observation video [O] . Lehtinen Ann-Mari 2006

• 1. Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements [J] . Chiman Kwan ,Bryan Chou ,Jonathan Yang . 信号与信息处理（英文） . 2019,第4期
• 2. A new approach for real time object detection and tracking on high resolution and multi-camera surveillance videos using GPU [J] . Mohammad Farukh Hashmi ,Ritu Pal ,Rajat Saxena . 中南大学学报 . 2016,第001期
• 3. 用于视频跟踪的帧转移型面阵CCD成像系统的研究 [C] . 李国宁 ,刘妍妍 ,金龙旭 . 2009年先进光学技术及其应用研讨会 . 2009
• 4. 基于关键帧的近重复视频检索 [A] . 罗红温 . 2015