Impact of new imaging modalities on video surveillance and invasion of privacy
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1 Impact of new imaging modalities on video surveillance and invasion of privacy Pavel Korshunov 25/11/14 VideoSense Winter School
2 The reality today 25/11/14 - VideoSense Winter School
3 Motivation 25/11/14 - VideoSense Winter School
4 How do the new imaging technologies impact privacy? 25/11/14 - VideoSense Winter School
5 Outline Privacy evaluation tools Subjective evaluation Crowdsourcing Objective metrics Datasets and evaluation results UHD HDR Mini-drones 25/11/14 - VideoSense Winter School
6 Privacy-intelligibility tradeoff Protect privacy obfuscate or remove personal information from the video Perform surveillance determine suspicious event/person, apprehend and prosecute criminals Where is the balance? 25/11/14 - VideoSense Winter School
7 Privacy filters Blurring, pixelization, masking, etc. Replacing an object with background or with another object Encryption and scrambling Geometrical filters a) b) c) d) e) f) g) b) c) Figure 3 Examples of privacy protection approaches: a) original image, b) pix with b=16, d) Gaussian blur with σ=8, e) Gaussian blur with σ=12, f) scrambli scrambling by random permutation. 25/11/14 - VideoSense Winter School
8 Evaluation tools Subjective evaluation Crowdsourcing Objective metrics 25/11/14 - VideoSense Winter School
9 Subjective evaluation Three naïve filters: blurring, pixelization, and masking Dataset of 10 annotated videos People acting normally or abnormally Wearing glasses, scarf, hats, etc. Blinking into the camera 25/11/14 - VideoSense Winter School
10 Example 25/11/14 - VideoSense Winter School
11 Questions asked race white asian I don t know Privacy gender female male I don t know glasses yes no I don t know sunglasses yes no I don t know Intelligibility scarf yes no I don t know blinking yes no I don t know 25/11/14 - VideoSense Winter School
12 Privacy Subjective results 1 blurring filter pixelization filter masking filter Intelligibility 25/11/14 - VideoSense Winter School
13 Evaluation tools Subjective evaluation Crowdsourcing Objective metrics 25/11/14 - VideoSense Winter School
14 What is crowdsourcing? Short and simple online tasks Large number of different workers Realistic settings A low cost alternative to subjective evaluations 25/11/14 - VideoSense Winter School
15 Facebook-based evaluations The same experiment as for subjective evaluations Facebook-based system Shows videos Collects answers Trusted workers 25/11/14 - VideoSense Winter School
16 Crowdsourcing: privacy blurring masking pixelization Online results Offline results 25/11/14 - VideoSense Winter School
17 Crowdsourcing: intelligibility blurring masking pixelization Online results Offline results 25/11/14 - VideoSense Winter School
18 Outcome Crowdsourcing is cheap and effective alternative to lab-based evaluations 25/11/14 - VideoSense Winter School
19 Evaluation tools Subjective evaluation Crowdsourcing Objective metrics 25/11/14 - VideoSense Winter School
20 Objective evaluations The cheapest and the most scalable option People counting in public transport Face recognition is the metric of privacy Face detection is the metric of intelligibility An ideal privacy protection filter Degrades face recognition Does not affect face detection 25/11/14 - VideoSense Winter School
21 Evaluation examples Increase strength of privacy filter Note relative decrease in accuracy of face detection and recognition 25/11/14 - VideoSense Winter School
22 Accuracy Blurring, FERET dataset Detection Recognition Gaussian kernel size 25/11/14 - VideoSense Winter School
23 Morphing, FERET dataset Recognition morphed Recognition recovered Accuracy interpolation_0 interpolation_0.2 interpolation_0.4 interpolation_0.6 interpolation_0.8 interpolation_1 Accuracy interpolation_0 interpolation_0.2 interpolation_0.4 interpolation_0.6 interpolation_0.8 interpolation_ Intensity strength Intensity strength 25/11/14 - VideoSense Winter School
24 Outcome Finding objective metrics for privacy and intelligibility is hard Context and content are important 25/11/14 - VideoSense Winter School
25 Outline Visual privacy filters Privacy-intelligibility tradeoff Evaluation tools Datasets Benchmarking Future challenges 25/11/14 - VideoSense Winter School
26 Outline Privacy evaluation tools Subjective evaluation Crowdsourcing Objective metrics Datasets and evaluation results UHD HDR Mini-drones 25/11/14 - VideoSense Winter School
27 Dataset design principles Wide range of surveillance scenarios Emphasis on personal visual information A mechanism to select privacy regions for evaluation New video capturing technologies 25/11/14 - VideoSense Winter School
28 Types of video data Ultra high definition (4K UHD) video High dynamic range (HDR) images and video Video from mini-drones 25/11/14 - VideoSense Winter School
29 Ultra High Definition (UHD) dataset 25/11/14 - VideoSense Winter School
30 UHD dataset Samsung Galaxy Note resolution video 26 surveillance sequences, 13 seconds Manually annotated body silhouettes and personal information UHD shows more details more privacy intrusive? Subjective evaluations UHD vs. HD vs. SD 25/11/14 - VideoSense Winter School
31 Walking example 25/11/14 - VideoSense Winter School
32 Exchanging bag example 25/11/14 - VideoSense Winter School
33 Fighting example 25/11/14 - VideoSense Winter School
34 Subjective evaluations of privacy 4K UHD Sony reference monitor Evaluation questions about Gender Race People s accessories Main action Visible items Accompanied questions on certainty 25/11/14 - VideoSense Winter School
35 % of correct answers Measuring privacy 25/11/14 - VideoSense Winter School
36 Measuring certainty 25/11/14 - VideoSense Winter School
37 High Dynamic Range (HDR) dataset 25/11/14 - VideoSense Winter School
38 Implications of HDR HDR surveillance cameras More details in the scene More privacy intrusive? Woman?? Man?? Middle Age?? Woman Young?? HDR monitor is needed Tone-mapped images Woman Elderly Wears sunglasses Man Middle Aged Woman Young 25/11/14 - VideoSense Winter School
39 HDR dataset 20 images and 68 video sequences Variety of lighting conditions, scenarios, gender, and race Images shot with Nikon D7100 camera, 5 exposures Video shot with BlackMagic Pocket Cinema Camera JAI AD-132 GE Bosch DINION 7000 HD 25/11/14 - VideoSense Winter School
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45 Crowdsourcing evaluations of images About 400 people participated Evaluation questions about Gender Race Age Color of clothes How many people 25/11/14 - VideoSense Winter School
46 Crowdsourcing results True HDR images LDR-like images 25/11/14 - VideoSense Winter School
47 Conclusions Privacy is a subjective notion Privacy is context and content dependent Privacy protection disrupts main utility Privacy protection and evaluation are challenging 25/11/14 - VideoSense Winter School
48 References subjective evaluations P. Korshunov, C. Araimo, F. De Simone, C. Velardo, and J.-L. Dugelay. Subjective Study of Privacy Filters in Video Surveillance. IEEE International Workshop on Multimedia Signal Processing (MMSP), Banff, Canada, P. Korshunov, S. Cai, and T. Ebrahimi. Crowdsourcing Approach for Evaluation of Privacy Filters in Video Surveillance. International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM'12), Nara, Japan, P. Korshunov, A. Melle, J.-L. Dugelay, and T. Ebrahimi. A framework for objective evaluation of privacy filters in video surveillance. Applications of Digital Image Processing XXXVI, 2013., San Diego, California, USA, Proceedings of SPIE, P. Korshunov, H. Nemoto, A. Skodras, and T. Ebrahimi. The effect of HDR images on privacy: crowdsourcing evaluation. SPIE Photonics Europe 2014, Optics, Photonics and Digital Technologies for Multimedia Applications, April 2014, Brussels, Belgium. P. Korshunov and T. Ebrahimi. Towards optimal distortion-based visual privacy filters. IEEE International Conference on Image Processing (ICIP), Paris, France, /11/14 - VideoSense Winter School
49 References datasets P. Korshunov and T. Ebrahimi. PEViD: privacy evaluation video dataset. Applications of Digital Image Processing XXXVI, 2013., San Diego, California, USA, Proceedings of SPIE, M. Rerabek, L. Yuan, L. Krasula, P. Korshunov, K. Fliegel, and Touradj Ebrahimi. Evaluation of privacy in high dynamic range video sequences. SPIE Optical Engineering + Applications, San Diego, California, USA, P. Korshunov and T. Ebrahimi. UHD Video Dataset for Evaluation of Privacy. Sixth International Workshop on Quality of Multimedia Experience (QoMEX 2014), Singapore, /11/14 - VideoSense Winter School
50 Disclaimer Some of the illustrations may have copyright constraints 25/11/14 VideoSense Winter School
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