Abstract
Smart surveillance systems become more meaningful if they both grow in reliability and robustness, while simultaneously offering a higher semantic level of understanding. To achieve a higher level of semantic scene understanding, the objects and their actions have to be interpreted in the given context, so that the extraction of contextual information is required. This chapter explores several techniques for extracting the contextual information such as spatial, motion, depth and co-occurrence, depending on applications. Afterwards, the chapter provides specific case studies to evaluate the usefulness of context information, based on: (1) region labeling of the surroundings of objects, (2) motion analysis of the water for moving ships, (3) traffic sign recognition for safety event evaluation and (4) the use of depth signals for obstacle detection. The chapter shows that the previous cases can be solved in an improved way with respect to robustness and semantic understanding. Case studies indicate up to 6.8% improvement of reliable correct object understanding and the novel possibility of labeling scene events as safe/unsafe depending on the object behavior and the detected surrounding context. In this chapter, it is shown that using contextual information improves automated video surveillance analysis, as it not only improves the reliability of moving object detection, but also enables scene understanding that is far beyond object understanding.
| Original language | English |
|---|---|
| Title of host publication | Emerging Research on Networked Multimedia Communication Systems |
| Editors | Dimitris Kanellopoulos |
| Publisher | IGI Global |
| Chapter | 5 |
| Pages | 159-203 |
| Number of pages | 45 |
| ISBN (Electronic) | 9781466688513 |
| ISBN (Print) | 1466688505, 9781466688506 |
| DOIs | |
| Publication status | Published - 14 Aug 2015 |
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Adding context information to video analysis for surveillance applications. / Javanbakhti, Solmaz; Bao, Xinfeng; Creusen, Ivo; Hazelhoff, Lykele; Sanberg, Willem P.; van de Wouw, Denis; Dubbelman, Gijs; Zinger, Svitlana; de With, Peter H.N.
Emerging Research on Networked Multimedia Communication Systems. ed. / Dimitris Kanellopoulos . IGI Global, 2015. p. 159-203.Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
TY - CHAP
T1 - Adding context information to video analysis for surveillance applications
AU - Javanbakhti, Solmaz
AU - Bao, Xinfeng
AU - Creusen, Ivo
AU - Hazelhoff, Lykele
AU - Sanberg, Willem P.
AU - van de Wouw, Denis
AU - Dubbelman, Gijs
AU - Zinger, Svitlana
AU - de With, Peter H.N.
PY - 2015/8/14
Y1 - 2015/8/14
N2 - Smart surveillance systems become more meaningful if they both grow in reliability and robustness, while simultaneously offering a higher semantic level of understanding. To achieve a higher level of semantic scene understanding, the objects and their actions have to be interpreted in the given context, so that the extraction of contextual information is required. This chapter explores several techniques for extracting the contextual information such as spatial, motion, depth and co-occurrence, depending on applications. Afterwards, the chapter provides specific case studies to evaluate the usefulness of context information, based on: (1) region labeling of the surroundings of objects, (2) motion analysis of the water for moving ships, (3) traffic sign recognition for safety event evaluation and (4) the use of depth signals for obstacle detection. The chapter shows that the previous cases can be solved in an improved way with respect to robustness and semantic understanding. Case studies indicate up to 6.8% improvement of reliable correct object understanding and the novel possibility of labeling scene events as safe/unsafe depending on the object behavior and the detected surrounding context. In this chapter, it is shown that using contextual information improves automated video surveillance analysis, as it not only improves the reliability of moving object detection, but also enables scene understanding that is far beyond object understanding.
AB - Smart surveillance systems become more meaningful if they both grow in reliability and robustness, while simultaneously offering a higher semantic level of understanding. To achieve a higher level of semantic scene understanding, the objects and their actions have to be interpreted in the given context, so that the extraction of contextual information is required. This chapter explores several techniques for extracting the contextual information such as spatial, motion, depth and co-occurrence, depending on applications. Afterwards, the chapter provides specific case studies to evaluate the usefulness of context information, based on: (1) region labeling of the surroundings of objects, (2) motion analysis of the water for moving ships, (3) traffic sign recognition for safety event evaluation and (4) the use of depth signals for obstacle detection. The chapter shows that the previous cases can be solved in an improved way with respect to robustness and semantic understanding. Case studies indicate up to 6.8% improvement of reliable correct object understanding and the novel possibility of labeling scene events as safe/unsafe depending on the object behavior and the detected surrounding context. In this chapter, it is shown that using contextual information improves automated video surveillance analysis, as it not only improves the reliability of moving object detection, but also enables scene understanding that is far beyond object understanding.
UR - http://www.scopus.com/inward/record.url?scp=84957937395&partnerID=8YFLogxK
U2 - 10.4018/978-1-4666-8850-6.ch005
DO - 10.4018/978-1-4666-8850-6.ch005
M3 - Chapter
AN - SCOPUS:84957937395
SN - 1466688505
SN - 9781466688506
SP - 159
EP - 203
BT - Emerging Research on Networked Multimedia Communication Systems
A2 - Kanellopoulos , Dimitris
PB - IGI Global
ER -