Enabling User Interactions with Video Contents Khalad Hasan, Yang Wang, Wing Kwong and Pourang Irani

Motion Control 2

3 End Call Voice Control

4 Face Recognition

5 Gaps Interaction with video contents Suitable querying and interface component Selection technique Motivation

6 Gaps Interaction with video contents Suitable querying and interface component Selection technique Motivation

7 Computer Vision Comparison among state-of-art algorithms Apply best algorithm to extract objects HCI Interaction with video contents Techniques for selection Contribution

Object Detection & Tracking 8

9 Tracking-Learning-Detection (TLD): Kalal et al. Datasets:

10 Struck: Hare et al.

11 StruckTLDStruckTLD Time(sec) Frame/Sec Coke Girl Tiger Tiger Average Speed Comparison

12 TLDStruck Coke Girl Tiger Tiger Average Precision

Interactions 13

14 Input Device

15 Kinect Input

Static Target Selection Target

Moving Target Selection

18 Selection Techniques Left-hand with Basic Left-hand with Ghost Left-hand with Crossing Depth with Basic Depth with Ghost Depth with Crossing

19 Selection Techniques Left-hand with Basic

Action Target Ghost (Khalad et al. CHI 2011)

21 Selection Techniques Left-hand with Ghost

22 Selection Techniques Left-hand with Crossing

23 Selection Techniques Selection Left-hand Depth

24 Results Technique Basic Ghost Crossing Task Completion Time (ms) 6,000 ― 4,000 ― 2,000 ― 0 ― Left-hand Depth

25 Results Technique Basic Ghost Crossing Average Number of Attempts 2.0 ― 1.5 ― 1.0 ― 0.5 ― 0 ― Left-hand Depth

26

27 Take-Home TLD is faster & accurate Both hands for Kinect based interactions Selection is best achieved with static proxies

28 Future work Computer Vision Multiple tracked objects Online detection & tracking HCI Selection Techniques New form of interactions with Kinect

Thank you