Object Detection for Crime Scene Evidence Analysis Using Deep Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-10-13

AUTHORS

Surajit Saikia , E. Fidalgo , Enrique Alegre , Laura Fernández-Robles

ABSTRACT

Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU. More... »

PAGES

14-24

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2

DOI

http://dx.doi.org/10.1007/978-3-319-68548-9_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092199466


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Electrical, Systems and Automation, University of Le\u00f3n, Le\u00f3n, Spain", 
            "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Saikia", 
        "givenName": "Surajit", 
        "id": "sg:person.013366561121.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013366561121.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Electrical, Systems and Automation, University of Le\u00f3n, Le\u00f3n, Spain", 
            "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fidalgo", 
        "givenName": "E.", 
        "id": "sg:person.012664070017.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012664070017.21"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Electrical, Systems and Automation, University of Le\u00f3n, Le\u00f3n, Spain", 
            "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alegre", 
        "givenName": "Enrique", 
        "id": "sg:person.016266057305.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016266057305.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Mechanical, Informatics and Aerospace Engineering, University of Le\u00f3n, Le\u00f3n, Spain", 
          "id": "http://www.grid.ac/institutes/grid.4807.b", 
          "name": [
            "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain", 
            "Department of Mechanical, Informatics and Aerospace Engineering, University of Le\u00f3n, Le\u00f3n, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fern\u00e1ndez-Robles", 
        "givenName": "Laura", 
        "id": "sg:person.010415303037.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010415303037.45"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2017-10-13", 
    "datePublishedReg": "2017-10-13", 
    "description": "Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12\u00a0s in Nvidia-TitanX GPU.", 
    "editor": [
      {
        "familyName": "Battiato", 
        "givenName": "Sebastiano", 
        "type": "Person"
      }, 
      {
        "familyName": "Gallo", 
        "givenName": "Giovanni", 
        "type": "Person"
      }, 
      {
        "familyName": "Schettini", 
        "givenName": "Raimondo", 
        "type": "Person"
      }, 
      {
        "familyName": "Stanco", 
        "givenName": "Filippo", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-68548-9_2", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-68547-2", 
        "978-3-319-68548-9"
      ], 
      "name": "Image Analysis and Processing - ICIAP 2017", 
      "type": "Book"
    }, 
    "keywords": [
      "object detection", 
      "subset of ImageNet", 
      "real-time systems", 
      "object of interest", 
      "surveillance applications", 
      "deep learning", 
      "crime scene analysis", 
      "object classes", 
      "security system", 
      "scene analysis", 
      "average accuracy", 
      "key modules", 
      "indoor environment", 
      "objects", 
      "large volumes", 
      "scene", 
      "law enforcement agencies", 
      "images", 
      "later analysis", 
      "ImageNet", 
      "CNN", 
      "GPU", 
      "video", 
      "enforcement agencies", 
      "evidence analysis", 
      "dataset", 
      "system", 
      "task", 
      "learning", 
      "detection", 
      "module", 
      "visual documentation", 
      "accuracy", 
      "environment", 
      "applications", 
      "effectiveness", 
      "documentation", 
      "work", 
      "data", 
      "subset", 
      "class", 
      "interest", 
      "analysis", 
      "time", 
      "significant role", 
      "specific crimes", 
      "police officers", 
      "crime", 
      "agencies", 
      "mean time", 
      "officers", 
      "volume", 
      "role", 
      "presence", 
      "Karina dataset", 
      "Nvidia-TitanX GPU", 
      "Crime Scene Evidence Analysis", 
      "Scene Evidence Analysis"
    ], 
    "name": "Object Detection for Crime Scene Evidence Analysis Using Deep Learning", 
    "pagination": "14-24", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1092199466"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-68548-9_2"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-68548-9_2", 
      "https://app.dimensions.ai/details/publication/pub.1092199466"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-11-01T19:00", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/chapter/chapter_437.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-68548-9_2"
  }
]
 

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.10.1007/978-3-319-68548-9_2'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'


 

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

159 TRIPLES      23 PREDICATES      83 URIs      76 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-68548-9_2 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N66507ceca0ae4d299082bc9aeb1bc5ec
4 schema:datePublished 2017-10-13
5 schema:datePublishedReg 2017-10-13
6 schema:description Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU.
7 schema:editor N514cb19bcd174dc0b7b308a3f4f75066
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N34d7a20c94b0425d9c29028d18fbe62c
12 schema:keywords CNN
13 Crime Scene Evidence Analysis
14 GPU
15 ImageNet
16 Karina dataset
17 Nvidia-TitanX GPU
18 Scene Evidence Analysis
19 accuracy
20 agencies
21 analysis
22 applications
23 average accuracy
24 class
25 crime
26 crime scene analysis
27 data
28 dataset
29 deep learning
30 detection
31 documentation
32 effectiveness
33 enforcement agencies
34 environment
35 evidence analysis
36 images
37 indoor environment
38 interest
39 key modules
40 large volumes
41 later analysis
42 law enforcement agencies
43 learning
44 mean time
45 module
46 object classes
47 object detection
48 object of interest
49 objects
50 officers
51 police officers
52 presence
53 real-time systems
54 role
55 scene
56 scene analysis
57 security system
58 significant role
59 specific crimes
60 subset
61 subset of ImageNet
62 surveillance applications
63 system
64 task
65 time
66 video
67 visual documentation
68 volume
69 work
70 schema:name Object Detection for Crime Scene Evidence Analysis Using Deep Learning
71 schema:pagination 14-24
72 schema:productId N7f840e6b1403488ca17cfa39163be9e8
73 Ne57ef9ad121d42039e3d061f58a291f9
74 schema:publisher Naf05da0e89f348539103f34effd7e32a
75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092199466
76 https://doi.org/10.1007/978-3-319-68548-9_2
77 schema:sdDatePublished 2021-11-01T19:00
78 schema:sdLicense https://scigraph.springernature.com/explorer/license/
79 schema:sdPublisher N54f790a02fac4fcf8d401a048d72df2b
80 schema:url https://doi.org/10.1007/978-3-319-68548-9_2
81 sgo:license sg:explorer/license/
82 sgo:sdDataset chapters
83 rdf:type schema:Chapter
84 N2534a33334e04b98a597a169b5526dd9 schema:familyName Schettini
85 schema:givenName Raimondo
86 rdf:type schema:Person
87 N28dc4e871a934498ad0340ea4d799d3a schema:familyName Battiato
88 schema:givenName Sebastiano
89 rdf:type schema:Person
90 N2c84bc03fb884d8cb8723b16bef14665 schema:familyName Gallo
91 schema:givenName Giovanni
92 rdf:type schema:Person
93 N32a57eed8dad47ebb5f1fbbd485c37d4 schema:familyName Stanco
94 schema:givenName Filippo
95 rdf:type schema:Person
96 N34d7a20c94b0425d9c29028d18fbe62c schema:isbn 978-3-319-68547-2
97 978-3-319-68548-9
98 schema:name Image Analysis and Processing - ICIAP 2017
99 rdf:type schema:Book
100 N350ed2b527854e9395a365be26b350df rdf:first sg:person.010415303037.45
101 rdf:rest rdf:nil
102 N39e6c3bdbf4a49aeb35b81bc064ea17f rdf:first N32a57eed8dad47ebb5f1fbbd485c37d4
103 rdf:rest rdf:nil
104 N514cb19bcd174dc0b7b308a3f4f75066 rdf:first N28dc4e871a934498ad0340ea4d799d3a
105 rdf:rest Nea3adcee89cb4e17bd54e4693a55daa0
106 N54f790a02fac4fcf8d401a048d72df2b schema:name Springer Nature - SN SciGraph project
107 rdf:type schema:Organization
108 N66507ceca0ae4d299082bc9aeb1bc5ec rdf:first sg:person.013366561121.26
109 rdf:rest N89c782401a9b4017833dbaeedc9da516
110 N7c9c88f5751a4f39a763b6b6b9bdcab1 rdf:first sg:person.016266057305.75
111 rdf:rest N350ed2b527854e9395a365be26b350df
112 N7f840e6b1403488ca17cfa39163be9e8 schema:name doi
113 schema:value 10.1007/978-3-319-68548-9_2
114 rdf:type schema:PropertyValue
115 N89c782401a9b4017833dbaeedc9da516 rdf:first sg:person.012664070017.21
116 rdf:rest N7c9c88f5751a4f39a763b6b6b9bdcab1
117 Naf05da0e89f348539103f34effd7e32a schema:name Springer Nature
118 rdf:type schema:Organisation
119 Nbed1fbeda4214c798f072fbcb502089e rdf:first N2534a33334e04b98a597a169b5526dd9
120 rdf:rest N39e6c3bdbf4a49aeb35b81bc064ea17f
121 Ne57ef9ad121d42039e3d061f58a291f9 schema:name dimensions_id
122 schema:value pub.1092199466
123 rdf:type schema:PropertyValue
124 Nea3adcee89cb4e17bd54e4693a55daa0 rdf:first N2c84bc03fb884d8cb8723b16bef14665
125 rdf:rest Nbed1fbeda4214c798f072fbcb502089e
126 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
127 schema:name Information and Computing Sciences
128 rdf:type schema:DefinedTerm
129 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
130 schema:name Artificial Intelligence and Image Processing
131 rdf:type schema:DefinedTerm
132 sg:person.010415303037.45 schema:affiliation grid-institutes:grid.4807.b
133 schema:familyName Fernández-Robles
134 schema:givenName Laura
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010415303037.45
136 rdf:type schema:Person
137 sg:person.012664070017.21 schema:affiliation grid-institutes:None
138 schema:familyName Fidalgo
139 schema:givenName E.
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012664070017.21
141 rdf:type schema:Person
142 sg:person.013366561121.26 schema:affiliation grid-institutes:None
143 schema:familyName Saikia
144 schema:givenName Surajit
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013366561121.26
146 rdf:type schema:Person
147 sg:person.016266057305.75 schema:affiliation grid-institutes:None
148 schema:familyName Alegre
149 schema:givenName Enrique
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016266057305.75
151 rdf:type schema:Person
152 grid-institutes:None schema:alternateName INCIBE (Spanish National Cybersecurity Institute), León, Spain
153 schema:name Department of Electrical, Systems and Automation, University of León, León, Spain
154 INCIBE (Spanish National Cybersecurity Institute), León, Spain
155 rdf:type schema:Organization
156 grid-institutes:grid.4807.b schema:alternateName Department of Mechanical, Informatics and Aerospace Engineering, University of León, León, Spain
157 schema:name Department of Mechanical, Informatics and Aerospace Engineering, University of León, León, Spain
158 INCIBE (Spanish National Cybersecurity Institute), León, Spain
159 rdf:type schema:Organization
 




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


...