Using location to see!
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1 Using location to see! Amir R. Zamir All rights of pictures/figures belong to the owners.
2 Phone!
3 Phone + Camera!
4 Phone + Camera + GPS!
5 Vision + Location! Phone + Camera + GPS!
6 Why not just camera? Why not just GPS? Vision + Location!
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12 Geo-localization Query Match Error: 7.6 m Query Match Error: 6.9 m
13 Where the user have visited? Input Video Desired output
14 Where the user have visited? Input Video Desired output
15 Object Detection Conventional Object Detection Location-assisted Object Detection, Smartphone Image
16 Not limited to mobile devices Geographical Data Organization Assisted Content Analysis on Mobile Devices Building 3D models [59]
17 Geo-spatial Perspective Three fundamental questions in Geo-spatial Analysis of Ground Level Images and Videos: 1) How to automatically geo-localize images and videos? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the geo-location in content analysis?
18 Automatic Geo-localization: Image geo-localization using Generalized Graphs Video Geo-localization and trajectory extraction Robust Refinement of geo-location using Random Walks Location-Aware image understating: Location-aware object detection Precise recognition of storefronts in images Paper: Image Geo-localization Based on Multiple Nearest Neighbor Feature Matching Using Generalized Graphs. In T-PAMI, 2014.
19 Where Am I? Problem: Accurate Image Localization Input Output Mere Visual Information (Images) Location in Terms of λ (Lon.) and φ (Lat.)
20 Where Am I? Problem: Accurate Image Localization Input Output Mere Visual Information (Images) Location in Terms of λ (Lon.) and φ (Lat.)
21 Google Maps Street View Dataset
22 Google Maps Street View Dataset
23 Street View Dataset Reference Images Place Marks Pittsburgh, PA Query Images
24 Ambiguity of local features =
25 Ambiguity of local features common in urban area Disambiguation using global features: Global Color Histogram GIST Geo-tags
26 Using Multiple Nearest Neighbors 1 st NN 2 nd NN 3 rd NN 4 th NN Detected Interest Points
27 Using Multiple Nearest Neighbors 1 st NN 2 nd NN 3 rd NN 4 th NN Detected Interest Points GMCP!
28 Using Multiple Nearest Neighbors 1 st NN 2 nd NN 3 rd NN 4 th NN Detected Interest Points GMCP!
29 Generalized Minimum Clique Problem Clusters of nodes.
30 Generalized Minimum Clique Problem Clusters of nodes. GMCP picks exactly one node out of each cluster The cost of the clique is minimized.
31 Forming Input Graph to GMCP NNs of one query feature
32 K-partite Complete Input Graph
33 K-partite Complete Input Graph
34 Generalized Minimum Clique
35 Generalized Minimum Clique Subset of NNs with maximum agreement in local and global features
36 From feature correspondences to GPS Query + SIFT features location
37 Vertical Axis: Number of Votes From feature correspondences to GPS location Geo-location PDF Horizontal Axes: Location Coordinates (x,y)
38 Geo-localization Results Query Match Error: 7.6 m Query Match Error: 6.9 m Query Match Error: m Query Match Error: 59.3 m
39 Geo-localization Results Query Match Error: 64.2 m Query Match Error: m Query Match Error: 6.4 m Query Match Error: m
40 Geo-localization Results
41 Automatic Geo-localization: Image geo-localization using Generalized Graphs Video Geo-localization and trajectory extraction Robust Refinement of geo-location using Random Walks Location-Aware image understating: Location-aware object detection Precise recognition of storefronts in images Paper: City Scale Geo-spatial Trajectory Estimation of a Moving Camera, In CVPR, 2012.
42 How about Videos?!
43 Our Approach
44 Segment Geo-Localization
45 Segment Geo-Localization Frame Geo-localization
46 Bayesian Filtering
47 Bayesian Filtering Enforces temporal consistency.
48 Bayesian Filtering Enforces temporal consistency. Likelihood (Current Segment)
49 Bayesian Filtering Prediction of the state from the previous state Prediction
50 Prediction (previous Segment) Likelihood (Current Segment) Bayesian Filtering
51 MST-based Trajectory Reconstruction
52 Geo-localization of a YouTube Video
53 Automatic Geo-localization: Image geo-localization using Generalized Graphs Video Geo-localization and trajectory extraction Robust Refinement of geo-location using Random Walks Location-Aware image understating: Location-aware object detection Precise recognition of storefronts in images Paper: GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor. In CVPR, 2014.
54 Why GPS-tag Refinement? What if the image is already geo-tagged? Internal GPS, WPS, Cell Signal Positioning, Manual tagging. Known issue: error in user shared geo-tags Mean=428 meters in 20% of data.
55 Block Diagram
56 Image Matching Query Image Matched Images
57 Triplet Location Estimation Query Image Matched Images
58 Triplet Location Estimation Trifocal Tensor Query Image Estimations on Map (ENU Metric System) Matched Images
59 Triplet Location Estimation Trifocal Tensor Query Image Estimations on Map (ENU Metric System) Matched Images
60 Triplet Location Estimation Trifocal Tensor Query Image Estimations on Map (ENU Metric System) Matched Images
61 Random Walks on GPS estimations
62 Refined Location Estimation Weighted mean using Stationary relevance scores:
63 Experimental Results User shared Images. (Flicker, Panoramio, Picasa) From San Francisco, CA; Pittsburgh, PA and Washington, DC. Test Set: 500 randomly selected subset.
64 Robustification Test Larger amount of noise in the input: Adaptive Damping Factor was used.
65 Automatic Geo-localization: Image geo-localization using Generalized Graphs Video Geo-localization and trajectory extraction Robust Refinement of geo-location using Random Walks Location-Aware image understating: Location-aware object detection Precise recognition of storefronts in images Paper: GIS-Assisted Object Detection and Geospatial Localization. In ECCV, 2014.
66 Location-aware Image Understanding Geo-localization: Estimate the location of the image/video. Geo-tag is known? Location-aware Image Understanding. Most images will be geo-tagged in the future. Particularly important for real world applications.
67 GIS Dataset e.g. Washington D.C. Lamp posts
68 GIS Dataset e.g. Washington D.C. Lamp posts Fire Hydrants
69 GIS Dataset Locations of most stationary objects are documented! e.g. Washington D.C. Buildings, Foliage, Road signs, ATMs, Fire Hydrants, Lamp posts, Cell/FM towers, Traffic Lights, Bus/subway stations, Trash cans.
70 GIS Object Detectors Fusion of Image content and GIS Lamp Post Road Sign Traffic Light
71 Location-aware Object Detection Object Detection (DPM) Image + Metadata Graph Matching Detected Objects Projecting GIS Objects Occlusion Handling
72 Obtaining Priors from GIS Camera View All Projections Non-occluded Projections Camera Matrix 2D projection on the Image GIS priors Camera Matrix
73 Higher Order Graph Matching New Object Detection Score Affine Fitting Score DPM Score
74 Query Image
75 DPM Results Loose Threshold Tuned Threshold Strict Threshold
76 GIS Projections DPM Results
77 Non Occluded GIS Projections DPM Results
78 Non Occluded GIS Projections Our Results
79 Traffic Signal, Street Light, and Fire Hydrant are detected successfully. Non Occluded GIS Projections Our Results
80 DPM Results (Tuned Threshold) Our Results
81 Quantitative Object Detection Results
82 Inverse of this process?! Geo-localization using the generic objects?! Cue #1: 2 lamp posts, 1 trash can, 1 fire hydrant. Cue #2: their geometric arrangement.
83 Geospatial Localization using Generic Objects Score for Presence of Objects Geometry Preserving Score(Graph Matching)
84 Geospatial Localization Example 1
85 Query Image
86 Object Detection Bounding Boxes
87 The Correct Location Retrieved as Rank 1
88 The subset of matching object detections and GIS projections are highlighted. The Correct Location Retrieved as Rank 1
89 The Correct Location Retrieved as Rank 1 High Score in Presence of Objects: lots of objects in common High Score in Geometry: Geometric arrangement of objects preserved
90 Automatic Geo-localization: Image geo-localization using Generalized Graphs Video Geo-localization and trajectory extraction Robust Refinement of geo-location using Random Walks Location-Aware image understating: Location-aware object detection Recognition of storefronts in images Paper: Visual Business Recognition - A Multimodal Approach. In ACM Multimedia, 2013.
91 Visual Business Recognition
92 Block Diagram Text Processing Input Image Business Lexicon Fusion Businesses Zorba s Gyros, Lush Cosmetic, Macy s, Italian Village Pizza, The Exchange, Easy Street, Bruegger s Bagels, Verizon Wireless, T. Verizon Image Matching
93 Business Lexicon An over-complete list of nearby businesses Yelp, Yellow Pages, City Grid, etc. Gentle Star Medspa, San Francisco Provident Loan Association, Walt Disney Concert Hall, Occasional Occasions, Casa de Campo, Golden Crown Paradise Resort, Great Parnassus Resort & Spa, Ketler Cleaning Service,Harden Yacht Services, Dicks Auto, The Law Offices of Andrew Gebelt, Will's Handyman, Verizon Wireless, Photography Woomer & Hall LLP, Apprehensive Patient Dental Office, Super 8 Motel Sun Prairie Dump-It Inc, Cvs Pharmacy Ray & Mari's Cleaning Service The Law Offics Of American Wills & Estates, Laurie, Mission St. Advisors, Everlasting, Ayoub Properties
94 Text Processing
95 Image Matching The image might not have text. Too complicated/cluttered text. Many relevant images available on the web
96 Image Matching Business Lexicon as key words business name, business name+city, business name+storefront.
97 Image Matching Between query and web images. Bag of words model.
98 Fusing Text Processing and Image Matching
99 Fusing Text Processing and Image Matching Probabilistic Late Fusion Verizon Wireless, Address: th Ave., Pittsburgh, PA USER Rating: 2/5
100 Text Detection Results Word Recognition Text Processing Fusion Image Matching
101 Text Detection Results Word Recognition Text Processing Image Matching
102 Results
103 Computational Complexity Image Geo-localization: sub-linear in search + local feature extraction Video Geo-localization: sub-linear in search + local feature extraction + Bayesian filtering (matrix multiplication) GPS-Tag Refinement: sub-linear in search + closed form solution for Random Walks GMCP Matching: NP-hard, polynomial approximation: O(KL^2 + LK^δ), L:= # of clusters, K:= # of nodes Object Detection: DPM + Graph Matching
104 Semantic Cross-View Matching F. Castalo, A. Zamir, R. Angst, S. Savarese
105 Semantic Wide-Baseline Matching
106 Semantic Map (GIS)
107 Semantic Map (GIS)
108 Topological and Semantic Matching
109 Semantic Segment Layout (SSL) features
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111 Experimental Results
112 Semantic Tree
113 Thank You! A. Zamir, F. Castaldo, R. Angst, S. Savarese, G. Vaca, S. Ardeshir, M. Shah PAMI'14: Image Geo-localization Based on Multiple-NN Feature Matching Using Generalized Graphs CVPR'14: GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor ECCV'14: GIS-Assisted Object Detection and Geospatial Localization ACM Multimedia'13: Visual Business Recognition - A Multimodal Approach CVPR'12: City Scale Geo-spatial Trajectory Estimation of a Moving Camera ICMLA'11: Identification of Commercial Entities in Street View Imagery ECCV'10: Accurate Image Localization Based on Google Maps Street View In Submission 15: Semantic Cross-View Matching
GIS-Assisted Object Detection and Geospatial Localization
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