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EV-Surv: Electronic and Visual Signals Integration for Efficient Surveillance

EV-Surv: Electronic and Visual Signals Integration for Efficient Surveillance. Dong Xuan, Ph.D. The Department of Computer Science and Engineering The Ohio-State University http://www.cse.ohio-state.edu/~xuan

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EV-Surv: Electronic and Visual Signals Integration for Efficient Surveillance

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  1. EV-Surv: Electronic and Visual Signals Integration for Efficient Surveillance Dong Xuan, Ph.D. The Department of Computer Science and Engineering The Ohio-State University http://www.cse.ohio-state.edu/~xuan Key Collaborators: Yuan F. Zheng, Jin Teng, Junda Zhu, Xinfeng Li, Boying Zhang and Qiang Zhai

  2. Outline Deficiency of Visual Surveillance Systems A Brief of Our EV-Surv System Case Studies A Broader View of Our EV-Surv System Final Remarks 2/55

  3. Visual Surveillance • Important for protecting public and personal security 3/55

  4. Visual Surveillance • Massive deployment in urban areas • Over 500 surveillance cameras in a Philadelphia neighborhood (below ) • New York has 4176 video cameras in lower Manhattan area [1]. 4/55

  5. Surveillance in Action Anomaly? Suspicious Action? Online monitoring Offline retrieval Finding all white males in red, medium stature, from Mon through Fri last week 5/55

  6. Failure Examples Chicago police installed 10,000 surveillance cameras in the city, only 1 of 200 crimes is captured by the visual surveillance [2]! In San Francisco, in the first three years after the city installed cameras, they helped police charge suspects in a grand total of six cases [2]! One of the bombers in London bombing (July, 2005) is not identified by the surveillance system and escaped [3]! 6/55

  7. Why fail? Visual technologies are not efficient and accurate enough to do automatic localization and tracking, and a lot of human power is needed! • Large volume of video data • Temporal: 2.07*106 frames per camera per day • Spatial: tons of surveillance cameras in a city • Monitored objects may be visually occluded or have multiple inconsistent appearance 7/55

  8. Outline Deficiency of Visual Surveillance Systems A Brief of Our EV-Surv System Case Studies A Broader View of Our EV-Surv System Final Remarks 8/55

  9. Our Methodology: E-V Integration Combining electronic and visual signals, such as GSM, 3G, WiFi, Bluetooth and NFC signals together for efficient surveillance E-V Integration makes it possible to efficiently and accurately localize and identify objects in large volume of video data 9/55

  10. Visual Signal-based Surveillance Who are they? • Can accurately localize and continuously track a person • But who is he or she? • Difficult to recognize people, e.g., through face recognition 10/55

  11. Electronic Signal-based Surveillance • Besides data exchanged, these communication channels also contain unique and identifiable electronic identities: • IMEI (International Mobile Equipment Identity), IMSI (International Mobile Subscriber Identity), WiFi, Bluetooth MAC addresses, RFID Number. Electronic Signals 11/55

  12. Pervasiveness of Electronic Signals Number Units Sold (millions) Source:Technology Review, Sept/Oct 2011 • Electronic signals are emitted by many mobile device • Mobile device’s popularity is increasing • Smartphone as an example: 450 million shipped in 2011 12/55

  13. Electronic Signal-based Surveillance -The with SIM card number 358985010745743 is here, -But the error can be as large as 100 meters Interferences, e.g., vehicles, building, humans etc. • Cannot accurately localize a person with a mobile device • Large error in localization due to interference • But can identify the device through electronic identifiers 13/55

  14. EV-Surv: A Bird’s Eye View 14/55

  15. EV-Surv: the Workflow • User Description: • -Features, Clothing • -Electronic Identity • Time Range • Area Visual signal Electronic signal Backend Database Visual signal Gateway E-V Integration Electronic signal Other Information Databases E-V Retrieval Visual signal Electronic signal All frames relevant to user inquiry 15/55

  16. Outline • Deficiency of Visual Surveillance Systems • A Brief of Our EV-Surv System • Case Studies • EV-Retrieval • EV-Tracking • A Broader View of Our EV-Surv System • Final Remarks 16/55

  17. Case Study I: EV-Retrieval Jin Teng, Junda Zhu, Boying Zhang, Dong Xuan and Yuan F. Zheng, “E-V: Efficient Visual Surveillance with Electronic Footprints”, IEEE INFOCOM 2012 Introducing electronic signals to help sort out videos for accurate and efficient person identification 17/55

  18. Person Identification A missing child A crime suspect Problem: Given the identity of a person, find his appearance Reference Photo Appearance in video • How a person of interest looks like at the time of surveillance • May be very different from any image or video of him in record • A type of retrieval 18/55

  19. Traditional ways Too Costly • Let people search through a huge pile of videos • If done automatically, computers need to extract all human figures in each frame and compare. 19/55

  20. E Signal-assisted Retrieval • With complementary electronic information, we can find out the electronic identity of the person, and use that information to guide our visual search • In critical surveillance context, it is possible to acquire this information • E.g., the government can request service providers to disclose the user information in anti-terrorism operations • Or, sometimes, it may be the case that we only have an electronic identity • FBI gets a suspicious conversion through phone tapping • Much smaller search space after screening with E signals! • Less processing burden on the V side 20/55

  21. Problem Formulation: Notations • V-sensing: V-ID and V Frame • V-ID: Visual identity, such as human figure • VID*: Our target V-ID • V Frame: a set of V-IDs with some background captured by visual sensors (cameras) in certain area and time • E-sensing: E-ID and E Frame • E-ID: Electronic identity such as MAC address etc. • EID*: Our target E-ID • E Frame: a set of E-IDs captured by electronic sensors in certain area and time 21/55

  22. Problem Formulation • Input: EID*, and a set of E frames and corresponding V frames • Output: VID* in video frames • We consider a baseline case with perfect E-IDs and V-IDs • All E-IDs and V-IDs are clear and distinguishable. No ambiguity. • No false positives or negatives in the detection and extraction of E-IDs and V-IDs • We will discuss more practical cases later 22/55

  23. A Basic Solution • Three steps: • Step 1: Find out all E frames which include EID* • Step 2: Find a subset of E frames, whose intersection is EID* • Step 3: Identify VID* in their corresponding V frames • Comments: Few V frames to process because V frames without VID* are filtered out, but there may be still many V frames 23/55

  24. Example Millions of E Frames E frame N+1 E frame 1 E frame 1 E frame 1 E frame 1 E frame 4 E frame 1 E frame 3 E frame 1 E frame 1 E frame 1 E frame 2 E frame 1 EID4 EID3 EID3 EID* EID* EID* EID* EID* EID* EID* EID* EID13 EID* EID* EID* EID* EID* EID2 EID1 EID5 EID3 EID7 EID3 EID4 EID3 EID2 …… EID12 EID3 EID91 EID2 EID2 EID2 EID2 EID2 EID2 EID2 EID4 Step 1: Extract all E frames with EID* …… 24/55

  25. Example (cont’d) Step 1 E frame 2 E frame 3 E frame 4 EID* EID2 EID* EID2 EID* EID5 EID3 V frame 1 E frame 1 E frame 1 Step 2 EID1 VID1 Step 3 EID* EID1 EID2 VID2 EID* VID* EID2 VID2 EID3 VID3 E frame 3 V frame 3 E frame 2 V frame 2 25/55

  26. A Better Solution • Find the minimum number of E Frames, whose intersection is the given E-ID, i.e. EID* • Further less frames for V side processing E frame 1 E frame 1 EID1 EID* EID1 EID* EID2 EID3 E frame 2 E frame 3 E frame 2 EID* EID3 EID* EID2 Two E Frames are enough identify EID* through intersection. EID2 26/55

  27. Nature of E-Filtering At least one 0 in each non-EID* column • Finding the minimum number of frames, whose intersection is EID* • NP-complete: equivalent to the set cover problem • Whether each E-ID appears in each E frame is summarized in a matrix, with 1 meaning ‘appear’ and 0 ‘not appear’. • At least one 0 in each non-EID* column • Use these 0s to ‘cover’ all non-EID* column (next page) 27/55

  28. Reduction to Set Cover Problem E frame 1 E frame 3 E frame 2 EID1 EID3 EID2 EID2 EID3 Set to be covered EID1 Cover EID2 EID2 Sets to cover Set Cover Problem: Find the fewest sets from a pool of sets (left hand side), whose union includes the set to be covered (right hand side) 28/55

  29. Solution: EDP Algorithm • Element Distinguishing Problem (EDP) • The element to be distinguished is EID* • Greedily select E Frames in which the most number of E-IDs can be told apart from EID* • In the example, the greedy algorithm will select e1 or e3 first, because we can tell two E-IDs are not EID* • Repeat the greedy selection until EID* is distinguishable 29/55

  30. EDP(cont’d) Approximation results can be achieved with the greedy heuristic algorithm for the set cover problem 30/55

  31. VID* Retrieval Find the corresponding VID* from the frames selected by E-Filtering. VID* is the only one that should appear in all the frames after E filtering. Find all V-IDs in the selected frames, then an intersection operation can give VID*. 31/55

  32. More Cases • Vagueness and completeness of V-ID/E-ID • Vagueness: reflect how clearly a V-ID/E-ID can be identified • Completeness: reflect if V-IDs/E-IDs are complete in a V/E frame (false positive/negative) √ √ √ √ √ √ √ √ The baseline case we have studied □ practical case of our focus addressed 32/55

  33. Practical Case I: Handling Vague V-IDs Same? • Vague V-IDs • Do not know for sure which person is which in different frames • Difficulty in the intersection of V frames to find VID* • Solution • nBM algorithm: find the VID with the largest probability of appearing in all V frames. 33/55

  34. The nBM Algorithm • Similarity matrix for all V-IDs which have appeared v1 v2 v3 • n-partite Best Match Problem (nBM) • Put all VIDs in different frames in n different circles • n-partite graph (right) 34/55

  35. nBM (cont’d) VID1 is in v2, and appears as VID2 VID1 is not in v2 • Maximum Likelihood matching • Given the observed VID1 … VIDm • Which VID is the best candidate • Calculate the probability of all VIDi across all V frames • Select the VID with the largest probability 35/55

  36. Solutions to Other Practical Cases Time 1 EIDi 0 smoothing 1 EIDi 0 1 EIDi 0 smoothing 1 EIDi 0 • Careful Deployment • Make sure that the coverage of the camera and the wireless detectors are roughly the same • nBM is probability based, so it is naturally resistant to noises • Select appropriate threshold in nBM for better tradeoff between noise resistance and performance • Generalized EDP • Handle missing/ghost E-ID • Introduction of fuzzy logic to improve the robustness of EDP • Use RSSI for estimation and smoothing 36/55

  37. Implementation • Real world implementation • One camera viewing from above to collect V frames • 1-3 laptops around sniffing the WiFi traffic to collect E frames • Tested on campus • Gymnasium • Library 37/55

  38. Case Study II: EV-Tracking • Combine E and V signals for more accurate localization and tracking • Preliminary work: EV-Loc Boying Zhang, Jin Teng, Junda Zhu, Xinfeng Li, Dong Xuan and Yuan F. Zheng. “EV-Loc: Integrating Electronic and Visual Signals for Accurate Localization”, in ACM MobiHoc12. 38/55

  39. EV-Loc for Localization E-IDi Visual Localization V-IDj Electronic Localization • Basic idea • Simultaneous E and V localization • Same localization result  E-IDi matches V-IDj • Can collect localization results over time and perform statistical matching 39/55

  40. Multiple Objects Localization Localized E object x3 y1 x1 Localized V object y1 x4 xi, yi: Localization results (coordinates) y3 x2 y4 x1 x2 x3 x4 π=(3, 1, 2, 4) y1 y2 y3 y4 Minimize sum of localization differences 40/55

  41. Nature of The Problem v1 e1 e2 v2 e3 v3 v4 e4 Objects’ EIDs Objects’ VIDs • Linear assignment problem (bipartite matching) • Minimum matching cost:argmin πiΣ||xi - yπi|| = ||x1 - y4|| + ||x2 - y3|| + ||x3 - y2|| + ||x4 – y1|| • We can use the Hungarian algorithm to solve this optimization problem 41/55

  42. EV-Loc for EV-Retrieval • In EV-Retrieval, we only use 0/1 to indicate E-IDs’ existence in E frames • However, we can improve it with EV-Loc • E-ID’s existence in a much smaller region • Easier for intersection 42/55

  43. EV-Loc for EV-Retrieval (cont’d) • Suppose all basestations can hear all mobile devices E frame 1 E frame 2 E frame 3 EID* EID* EID1 EID1 EID1 EID* E frame 1 EID* EID1 E frame 2 EID* EID1 EID1 EID1 EID* EID0 E frame 3 EID* EID1 Possible location Original Scheme Now with Localization 43/55

  44. Outline Deficiency of Visual Surveillance Systems A Brief of Our EV-Surv System Case Studies A Broader View of Our EV-Surv System Final Remarks 44/55

  45. EV Surveillance: Problem Space Uncooperative Cooperative Tracking Onsite Offline 45/55

  46. Problem Space (Cont’d) • X: Tracking: offline or onsite • Y: Object of monitoring: • Individual : non-coordinative • Group: coordinative, tied by relation in terms of vicinity, social relationship, appearance, action/behavior • Z: Object friendliness: • Cooperative: E/V signals on purposely for easy tracking • Uncooperative: neutral (E/V signal on/off follows its own cause), and even misleading • Other possible dimensions: objects can be human and vehicles etc. 46/55

  47. Typical Cases • Case 1: <X: offline tracking, Y: individual object, Z: cooperative), i.e. offline tracking of individual and cooperative object • E.g.missing elders searching • Case 2: <X: offline tracking, Y: group objects, Z: cooperative) , i.e. offline tracking of group and cooperative objects • E.g. public health monitoring • Case 3: <X: onsite tracking, Y: individual/group objects, Z: cooperative), i.e. onsite tracking of individual/group and cooperative objects • E.g. sports training/traffic monitoring • Case 4: <X: offline/onsite tracking, Y: individual object/group objects, Z: uncooperative, i.e. tracking of uncooperative objects • E.g. criminal tracking 47/55

  48. Open Issues • E-V sensor deployment • Electronic signal capturing • E-V data analysis • Privacy • Real-world implementation and evaluation 48/55

  49. Electronic and Visual Sensor Deployment Electronic sensor deployment problem Visual sensor deployment problem How to do E/V sensors joint optimal deployment? 49/55

  50. Electronic Signal Capturing Mobile Devices emit electronic signals at different time Electronic interference is always there Non-cooperative targets exist Different electronic signal capturing times vary 50/55

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