Mobile Surveillance by 3D-Outlier Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Peter Holzer , Axel Pinz

ABSTRACT

We present a novel online method to model independent foreground motion by using solely traditional structure and motion (S+M) algorithms. On the one hand, the visible static scene can be reconstructed and on the other hand, the position and orientation (pose) of the observer (mobile camera) are estimated. Additionally, we use 3D-outlier analysis for foreground motion detection and tracking. First, we cluster the available 3D-information such that, with high probability, each cluster corresponds to a moving object. Next, we establish a purely geometry-based object representation that can be used to reliably estimate each object’s pose. Finally, we extend the purely geometry-based object representation and add local descriptors to solve the loop closing problem for the underlying S+M algorithm. Experimental results on single and multi-object video data demonstrate the viability of this method. Major results include the computation of a stable representation of moving foreground objects, basic recognition possibilities due to descriptors, and motion trajectories that can be used for motion analysis of objects. Our novel multibody structure and motion (MSaM) approach runs online and can be used to control active surveillance systems in terms of dynamic scenes, observer pose, and observer-to-object pose estimation, or to enrich available information in existing appearance- and shape-based object categorization. More... »

PAGES

195-204

References to SciGraph publications

Book

TITLE

Computer Vision – ACCV 2010 Workshops

ISBN

978-3-642-22821-6
978-3-642-22822-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-22822-3_20

DOI

http://dx.doi.org/10.1007/978-3-642-22822-3_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016265039


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Graz University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.410413.3", 
          "name": [
            "Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Austria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Holzer", 
        "givenName": "Peter", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graz University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.410413.3", 
          "name": [
            "Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Austria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pinz", 
        "givenName": "Axel", 
        "id": "sg:person.012033065653.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012033065653.49"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-540-88688-4_59", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004249494", 
          "https://doi.org/10.1007/978-3-540-88688-4_59"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-88688-4_59", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004249494", 
          "https://doi.org/10.1007/978-3-540-88688-4_59"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1008000628999", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007537933", 
          "https://doi.org/10.1023/a:1008000628999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-45054-8_58", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007884336", 
          "https://doi.org/10.1007/3-540-45054-8_58"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11263-007-0111-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030232356", 
          "https://doi.org/10.1007/s11263-007-0111-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11744085_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037854667", 
          "https://doi.org/10.1007/11744085_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11744085_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037854667", 
          "https://doi.org/10.1007/11744085_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.1000236", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061155588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.1975.1055330", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061647566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2007.1049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2008.170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743528"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2010.23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743942"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.1994.323794", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093488775"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wacv.2008.4544016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093904816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2007.383090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093963306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2010.5539794", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094039259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2007.4409115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094420395"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ismar.2007.4538852", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094578863"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2008.4587581", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094973882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.1995.466815", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095748591"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011", 
    "datePublishedReg": "2011-01-01", 
    "description": "We present a novel online method to model independent foreground motion by using solely traditional structure and motion (S+M) algorithms. On the one hand, the visible static scene can be reconstructed and on the other hand, the position and orientation (pose) of the observer (mobile camera) are estimated. Additionally, we use 3D-outlier analysis for foreground motion detection and tracking. First, we cluster the available 3D-information such that, with high probability, each cluster corresponds to a moving object. Next, we establish a purely geometry-based object representation that can be used to reliably estimate each object\u2019s pose. Finally, we extend the purely geometry-based object representation and add local descriptors to solve the loop closing problem for the underlying S+M algorithm. Experimental results on single and multi-object video data demonstrate the viability of this method. Major results include the computation of a stable representation of moving foreground objects, basic recognition possibilities due to descriptors, and motion trajectories that can be used for motion analysis of objects. Our novel multibody structure and motion (MSaM) approach runs online and can be used to control active surveillance systems in terms of dynamic scenes, observer pose, and observer-to-object pose estimation, or to enrich available information in existing appearance- and shape-based object categorization.", 
    "editor": [
      {
        "familyName": "Koch", 
        "givenName": "Reinhard", 
        "type": "Person"
      }, 
      {
        "familyName": "Huang", 
        "givenName": "Fay", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-22822-3_20", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-22821-6", 
        "978-3-642-22822-3"
      ], 
      "name": "Computer Vision \u2013 ACCV 2010 Workshops", 
      "type": "Book"
    }, 
    "name": "Mobile Surveillance by 3D-Outlier Analysis", 
    "pagination": "195-204", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1016265039"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-22822-3_20"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4d7fde14517fb274313fbde116c972b75d45f45379854a74c39b534cda90bdd6"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-22822-3_20", 
      "https://app.dimensions.ai/details/publication/pub.1016265039"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T09:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000371_0000000371/records_130829_00000001.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-642-22822-3_20"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22822-3_20'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22822-3_20'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22822-3_20'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22822-3_20'


 

This table displays all metadata directly associated to this object as RDF triples.

135 TRIPLES      23 PREDICATES      45 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-22822-3_20 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Neb0137a391d14969aa9b25b14bc3e00b
4 schema:citation sg:pub.10.1007/11744085_8
5 sg:pub.10.1007/3-540-45054-8_58
6 sg:pub.10.1007/978-3-540-88688-4_59
7 sg:pub.10.1007/s11263-007-0111-7
8 sg:pub.10.1023/a:1008000628999
9 https://doi.org/10.1109/34.1000236
10 https://doi.org/10.1109/cvpr.1994.323794
11 https://doi.org/10.1109/cvpr.2007.383090
12 https://doi.org/10.1109/cvpr.2008.4587581
13 https://doi.org/10.1109/cvpr.2010.5539794
14 https://doi.org/10.1109/iccv.1995.466815
15 https://doi.org/10.1109/iccv.2007.4409115
16 https://doi.org/10.1109/ismar.2007.4538852
17 https://doi.org/10.1109/tit.1975.1055330
18 https://doi.org/10.1109/tpami.2007.1049
19 https://doi.org/10.1109/tpami.2008.170
20 https://doi.org/10.1109/tpami.2010.23
21 https://doi.org/10.1109/wacv.2008.4544016
22 schema:datePublished 2011
23 schema:datePublishedReg 2011-01-01
24 schema:description We present a novel online method to model independent foreground motion by using solely traditional structure and motion (S+M) algorithms. On the one hand, the visible static scene can be reconstructed and on the other hand, the position and orientation (pose) of the observer (mobile camera) are estimated. Additionally, we use 3D-outlier analysis for foreground motion detection and tracking. First, we cluster the available 3D-information such that, with high probability, each cluster corresponds to a moving object. Next, we establish a purely geometry-based object representation that can be used to reliably estimate each object’s pose. Finally, we extend the purely geometry-based object representation and add local descriptors to solve the loop closing problem for the underlying S+M algorithm. Experimental results on single and multi-object video data demonstrate the viability of this method. Major results include the computation of a stable representation of moving foreground objects, basic recognition possibilities due to descriptors, and motion trajectories that can be used for motion analysis of objects. Our novel multibody structure and motion (MSaM) approach runs online and can be used to control active surveillance systems in terms of dynamic scenes, observer pose, and observer-to-object pose estimation, or to enrich available information in existing appearance- and shape-based object categorization.
25 schema:editor N780617b30dc24e04bb2edd8cb69d9b2c
26 schema:genre chapter
27 schema:inLanguage en
28 schema:isAccessibleForFree false
29 schema:isPartOf N26f9a530d54641e0a1dd6cf0a9bd29a5
30 schema:name Mobile Surveillance by 3D-Outlier Analysis
31 schema:pagination 195-204
32 schema:productId N415dd30c2fb5454992cbd13af4fb8bc5
33 N7a48367302764c1d8888317efebc4e3a
34 Nf8b8e5ec90ea4417b33290b3cf40710c
35 schema:publisher N54429c043fba48c3a40a713c8129ce13
36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016265039
37 https://doi.org/10.1007/978-3-642-22822-3_20
38 schema:sdDatePublished 2019-04-16T09:20
39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
40 schema:sdPublisher Ne36d781976eb40ee878b34972256a608
41 schema:url https://link.springer.com/10.1007%2F978-3-642-22822-3_20
42 sgo:license sg:explorer/license/
43 sgo:sdDataset chapters
44 rdf:type schema:Chapter
45 N26f9a530d54641e0a1dd6cf0a9bd29a5 schema:isbn 978-3-642-22821-6
46 978-3-642-22822-3
47 schema:name Computer Vision – ACCV 2010 Workshops
48 rdf:type schema:Book
49 N2e7612507552442082361a9c357c7018 schema:affiliation https://www.grid.ac/institutes/grid.410413.3
50 schema:familyName Holzer
51 schema:givenName Peter
52 rdf:type schema:Person
53 N3cd4ef9a2b704739afe1165c9a504da1 rdf:first Nc6da92d15a55446aa1b3f1b354432ef7
54 rdf:rest rdf:nil
55 N415dd30c2fb5454992cbd13af4fb8bc5 schema:name readcube_id
56 schema:value 4d7fde14517fb274313fbde116c972b75d45f45379854a74c39b534cda90bdd6
57 rdf:type schema:PropertyValue
58 N4236ce69d683457f95897372fbb9a064 schema:familyName Koch
59 schema:givenName Reinhard
60 rdf:type schema:Person
61 N54429c043fba48c3a40a713c8129ce13 schema:location Berlin, Heidelberg
62 schema:name Springer Berlin Heidelberg
63 rdf:type schema:Organisation
64 N780617b30dc24e04bb2edd8cb69d9b2c rdf:first N4236ce69d683457f95897372fbb9a064
65 rdf:rest N3cd4ef9a2b704739afe1165c9a504da1
66 N7a48367302764c1d8888317efebc4e3a schema:name dimensions_id
67 schema:value pub.1016265039
68 rdf:type schema:PropertyValue
69 Na41b6529ea15442cabea1b1697968a5d rdf:first sg:person.012033065653.49
70 rdf:rest rdf:nil
71 Nc6da92d15a55446aa1b3f1b354432ef7 schema:familyName Huang
72 schema:givenName Fay
73 rdf:type schema:Person
74 Ne36d781976eb40ee878b34972256a608 schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 Neb0137a391d14969aa9b25b14bc3e00b rdf:first N2e7612507552442082361a9c357c7018
77 rdf:rest Na41b6529ea15442cabea1b1697968a5d
78 Nf8b8e5ec90ea4417b33290b3cf40710c schema:name doi
79 schema:value 10.1007/978-3-642-22822-3_20
80 rdf:type schema:PropertyValue
81 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
82 schema:name Information and Computing Sciences
83 rdf:type schema:DefinedTerm
84 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
85 schema:name Artificial Intelligence and Image Processing
86 rdf:type schema:DefinedTerm
87 sg:person.012033065653.49 schema:affiliation https://www.grid.ac/institutes/grid.410413.3
88 schema:familyName Pinz
89 schema:givenName Axel
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012033065653.49
91 rdf:type schema:Person
92 sg:pub.10.1007/11744085_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037854667
93 https://doi.org/10.1007/11744085_8
94 rdf:type schema:CreativeWork
95 sg:pub.10.1007/3-540-45054-8_58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007884336
96 https://doi.org/10.1007/3-540-45054-8_58
97 rdf:type schema:CreativeWork
98 sg:pub.10.1007/978-3-540-88688-4_59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004249494
99 https://doi.org/10.1007/978-3-540-88688-4_59
100 rdf:type schema:CreativeWork
101 sg:pub.10.1007/s11263-007-0111-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030232356
102 https://doi.org/10.1007/s11263-007-0111-7
103 rdf:type schema:CreativeWork
104 sg:pub.10.1023/a:1008000628999 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007537933
105 https://doi.org/10.1023/a:1008000628999
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1109/34.1000236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061155588
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1109/cvpr.1994.323794 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093488775
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1109/cvpr.2007.383090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093963306
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1109/cvpr.2008.4587581 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094973882
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1109/cvpr.2010.5539794 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094039259
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1109/iccv.1995.466815 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095748591
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1109/iccv.2007.4409115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094420395
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1109/ismar.2007.4538852 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094578863
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1109/tit.1975.1055330 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061647566
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1109/tpami.2007.1049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743181
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1109/tpami.2008.170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743528
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1109/tpami.2010.23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743942
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/wacv.2008.4544016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093904816
132 rdf:type schema:CreativeWork
133 https://www.grid.ac/institutes/grid.410413.3 schema:alternateName Graz University of Technology
134 schema:name Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Austria
135 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...