Eulerian Video Magnification Baby Monitor. Nik Cimino
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1 Eulerian Video Magnification Baby Monitor Nik Cimino
2 Eulerian Video Magnification Wu, Hao-Yu, et al. "Eulerian video magnification for revealing subtle changes in the world." ACM Trans. Graph (2012): 65. Spatially Decompose (e.g. Laplacian Pyramid) Temporally Filter Amplify Reconstruct
3 Considerations Used 30FPS camera, 15Hz is the fastest detectable frequency (Nyquist theorem), yields 33ms/frame: T frame = 1 30fps = 1s 30frames = s 1frame = 33.33ms 1frame Higher resolutions yield better signal detection Use good sensor/good lighting, high ISO = high noise Minimize camera and subject motion Compression can cause problems, inter-frame redundancy and loss of low-level details
4 Goal Detect babies heart rate from spatial magnification Detect babies respiration rate from movement magnification
5 Videos Tested and Referenced Side Video Front Video Swing Video
6 Area of Interest Selection Manual for now Future: Face recognition Skin tone detection Area of motion could possibly be revealed by average image minus current Side Video
7 Algorithms Tested Gaussian blur and down sample Ideal bandpass temporal filter Laplacian Pyramid Ideal bandpass temporal filter Butterworth (subtraction of two lowpass filters) temporal filter IIR (subtraction of two lowpass filters) temporal filter y1[n] = r1*x[n] + (1-r1)*y1[n-1] y2[n] = r2*x[n] + (1-r2)*y2[n-1] (r1 > r2) y[n] = y1[n] - y2[n]
8 Algorithm Parameters Alpha - Amplification Factor (applies to pyramid levels 1, 2, 3) [verify] Levels - Number of levels to use in the pyramids Frequency Low - Low end of our band pass Frequency High - High end of our band pass Frames per Second - FPS of the film, needed to accurately perform temporal filtering Chrome Attenuation - Similar to alpha but only applied to pyramid levels 2 and 3.
9 Front Video Example Post-Processed
10 Single Pixel Slice of Time Front Video
11 Spatiotemporal Profile Using that single pixel slice a spatiotemporal profile is created Visualize spatial amplification Front Video : Face
12 BPM Extraction Attempt 1 Using Otsu s threshold method Did not work as desired Front Video : Face
13 BPM Extraction Attempt 1 (con t) Again with Otsu s threshold method Side Video : Face
14 BPM Extraction Attempt 1 (con t) Manual Threshold This is better, but still not reliable Side Video : Face
15 BPM Extraction Attempt 2 Fourier Analysis Proved to be robust for dominant frequency detection Clearly shows dominant bands Side Video : Face
16 Single Pixel Intensity versus Frames Intensity Frames 0-~1500 (@ 33.33ms/frame) Swing Video : Face
17 All Pixel Frequency Response 7 x 10-3 Frequency Response Frequency (Hz) Swing Video : Face
18 Average Frequency Response 6 x 10-3 Frequency Response Frequency (Hz) Swing Video : Face
19 Average Frequency Response of Interest Hz (40 BPM) to 3 Hz (180 BPM) Frequency Response Frequency (Hz) Swing Video : Face
20 Movement Recovery Notice that very soon after movement the spatiotemporal profile shows recovery Swing Video : Face
21 Average Frequency Response 7 x 10-3 Frequency Response 12 x 10-3 Frequency Response Frequency (Hz) Frequency (Hz) Side Video : Face Front Video : Face
22 Average Frequency Response of Interest 14 Frequency Response of Interest 7 Frequency Response of Interest Frequency (Hz) Frequency (Hz) Side Video : Face Front Video : Face
23 Robust Spatial Detection Side video has a very small amount of pixels showing face, but still detected accurately Front video is dark and has a fair amount of noise, but the frequency response is still good Swing video had movement and yet recovered very well
24 Spatiotemporal Profiles Front Video : Chest Swing Video : Chest Side Video : Chest
25 Frequency Responses 5 x 10-3 Frequency Response Frequency Response x Frequency Response Frequency (Hz) Frequency (Hz) Swing Video : Chest 4 3 Front Video : Chest Frequency (Hz) Side Video : Chest
26 Frequency Responses 90 Frequency Response of Interest 30 Frequency Response of Interest Frequency Response of Interest Frequency (Hz) Swing Video : Chest Frequency (Hz) Front Video : Chest 0.2 Hz (12 BPM) to 0.8 Hz (50 BPM) Frequency (Hz) Side Video : Chest
27 Poor Motion Magnification There isn t a clean band where the respiratory rate should ve been The spatiotemporal profiles have very flat areas Calculated dominant frequencies aren t too far off, but the numbers have no confidence associated with them
28 Face Output Front : Face - Detected Dominant Frequency at (Hz): Dominant BPM: 156 < tested 151 beats per minute Side : Face - Detected Dominant Frequency at (Hz): Dominant BPM: 122 < tested 130 beats per minute Swing : Face - Detected Dominant Frequency at (Hz): Dominant BPM (possible interference resonating at 2Hz): 120 < tested 144 beats per minute
29 Chest Output Swing : Chest - Detected Dominant Frequency at (Hz): Dominant BPM: 39 < tested 38 respirations per minute Side : Chest - Detected Dominant Frequency at (Hz): Dominant BPM: 49 < tested 29 respirations per minute Detected Dominant Frequency at (Hz): Dominant BPM: 47 < tested 33 respirations per minute
30 Conclusions Eulerian video magnification is surprisingly robust for spatial magnification Movement magnification works, but is not as reliable Laplacian pyramid magnification worked best for motion magnification Gaussian blur and down sample worked best for spatial amplification
31 Future Work Add area of interest selection for video Change BPM detection to be on a small time interval Change dominant BPM detection to be a dominant band detection Stitch both chest and face into same video with numerical BPM for heart and respiratory rates Try phase based approach for movement magnification
32 Questions? Face Heart Rate Front : Side : Swing : 53 seconds for noise Chest Respiration Rate Front : Side : Swing : 53 seconds for noise
33 Phase-Based Magnification Uses phase in Fourier domain (translation) Wadhwa, Neal, et al. "Phase-based video motion processing." ACM Transactions on Graphics (TOG) 32.4 (2013): 80.
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