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Published byGeoffrey Phillips Modified over 9 years ago
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Enabling User Interactions with Video Contents Khalad Hasan, Yang Wang, Wing Kwong and Pourang Irani
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Motion Control 2
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3 End Call Voice Control
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4 Face Recognition
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5 Gaps Interaction with video contents Suitable querying and interface component Selection technique Motivation
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6 Gaps Interaction with video contents Suitable querying and interface component Selection technique Motivation
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7 Computer Vision Comparison among state-of-art algorithms Apply best algorithm to extract objects HCI Interaction with video contents Techniques for selection Contribution
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Object Detection & Tracking 8
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9 Tracking-Learning-Detection (TLD): Kalal et al. Datasets: http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
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10 Struck: Hare et al.
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11 StruckTLDStruckTLD Time(sec) Frame/Sec Coke251471.166.21 Girl401931.325.69 Tiger1394530.906.68 Tiger2334621.095.89 Average345.00 63.75 1.126.12 Speed Comparison
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12 TLDStruck Coke0.940.69 Girl0.930.80 Tiger10.860.77 Tiger20.790.63 Average0.880.72 Precision
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Interactions 13
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14 Input Device
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15 Kinect Input
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Static Target Selection Target
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Moving Target Selection
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18 Selection Techniques Left-hand with Basic Left-hand with Ghost Left-hand with Crossing Depth with Basic Depth with Ghost Depth with Crossing
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19 Selection Techniques Left-hand with Basic
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Action Target Ghost (Khalad et al. CHI 2011)
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21 Selection Techniques Left-hand with Ghost
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22 Selection Techniques Left-hand with Crossing
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23 Selection Techniques Selection Left-hand Depth
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24 Results Technique Basic Ghost Crossing Task Completion Time (ms) 6,000 ― 4,000 ― 2,000 ― 0 ― Left-hand Depth
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25 Results Technique Basic Ghost Crossing Average Number of Attempts 2.0 ― 1.5 ― 1.0 ― 0.5 ― 0 ― Left-hand Depth
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27 Take-Home TLD is faster & accurate Both hands for Kinect based interactions Selection is best achieved with static proxies
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28 Future work Computer Vision Multiple tracked objects Online detection & tracking HCI Selection Techniques New form of interactions with Kinect
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Thank you
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