%PDF-1.3 1 0 obj << /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] /Type /Pages /Count 9 >> endobj 2 0 obj << /Title (s\056bano\100dundee\056ac\056uk\073 s\056j\056z\056mckenna\100dundee\056ac\056uk\073 j\056n\056zhang\100dundee\056ac\056uk) /Producer (PyPDF2) /Author (Focused interaction occurs when co\055present individuals\054 having mutual focus of attention\054 interact by establishing face\055to\055face engagement and direct conversation\056 Face\055to\055face engagement is often not maintained throughout the entirety of a focused interaction\056 In this paper\054 we present an online method for automatic classification of unconstrained egocentric \050first\055person perspective\051 videos into segments having no focused interaction\054 focused interaction when the camera wearer is stationary and focused interaction when the camera wearer is moving\056 We extract features from both audio and video data streams and perform temporal segmentation by using support vector machines with linear and non\055linear kernels\056 We provide empirical evidence that fusion of visual face track scores\054 camera motion profile and audio voice activity scores is an effective combination for focused interaction classification\056 ) /Subject (2017 IEEE International Conference on Computer Vision) >> endobj 3 0 obj << /Parent 1 0 R /Rotate 0 /Type /Page /Contents 13 0 R /Resources << /XObject << /x8 14 0 R /x6 17 0 R /x12 20 0 R /x10 23 0 R >> /ExtGState << /s9 26 0 R /R29 29 0 R /s11 30 0 R /a0 << /CA 1 /ca 1 >> /s5 33 0 R /s7 36 0 R >> /Font << /F2 39 0 R /F1 40 0 R /R38 41 0 R /R36 45 0 R /R34 48 0 R /R32 52 0 R /R30 56 0 R >> /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] >> /MediaBox [ 0 0 612 792 ] /Annots [ 60 0 R 61 0 R 62 0 R 63 0 R 64 0 R 65 0 R 66 0 R 67 0 R 68 0 R 69 0 R 70 0 R 71 0 R 72 0 R 73 0 R 74 0 R 75 0 R 76 0 R 77 0 R 78 0 R ] >> endobj 4 0 obj << /Parent 1 0 R /Rotate 0 /Type /Page /Contents 79 0 R /Resources << /XObject << /R67 80 0 R 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