Standard
A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties. / Sadeghi-Tehran, Pouria
; Angelov, Plamen; Ramezani, Ramin.
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications : 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part II. ed. / Eyke Hüllermeier; Rudolf Kruse; Frank Hoffmann. Berlin: Springer, 2010. p. 30-43 (Communications in Computer and Information Science; Vol. 81).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Sadeghi-Tehran, P
, Angelov, P & Ramezani, R 2010,
A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties. in E Hüllermeier, R Kruse & F Hoffmann (eds),
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications : 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part II. Communications in Computer and Information Science, vol. 81, Springer, Berlin, pp. 30-43, International Conference on Information Processing and Uncertainty Management, IPMU 2010, Dortmund, Germany,
28/06/10.
https://doi.org/10.1007/978-3-642-14058-7_4APA
Sadeghi-Tehran, P.
, Angelov, P., & Ramezani, R. (2010).
A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties. In E. Hüllermeier, R. Kruse, & F. Hoffmann (Eds.),
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications : 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part II (pp. 30-43). (Communications in Computer and Information Science; Vol. 81). Springer.
https://doi.org/10.1007/978-3-642-14058-7_4Vancouver
Sadeghi-Tehran P
, Angelov P, Ramezani R.
A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties. In Hüllermeier E, Kruse R, Hoffmann F, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications : 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part II. Berlin: Springer. 2010. p. 30-43. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-14058-7_4
Author
Sadeghi-Tehran, Pouria
; Angelov, Plamen ; Ramezani, Ramin. /
A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications : 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part II. editor / Eyke Hüllermeier ; Rudolf Kruse ; Frank Hoffmann. Berlin : Springer, 2010. pp. 30-43 (Communications in Computer and Information Science).
Bibtex
@inproceedings{739191b6b39e4f6796f28369d7664360,
title = "A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties",
abstract = "Real-time processing the information coming form video, infra-red or electro-optical sources is a challenging task due the uncertainties such as noise and clutter, but also due to the large dimensionalities of the problem and the demand for fast and efficient algorithms. This paper details an approach for automatic detection, single and multiple objects identification and tracking in video streams with applications to surveillance, security and autonomous systems. It is based on a method that provides recursive density estimation (RDE) using a Cauchy type of kernel. The main advantage of the RDE approach as compared to other traditional methods (e.g. KDE) is the low computational and memory storage cost since it works on a frame-by-frame basis; the lack of thresholds, and applicability to multiple objects identification and tracking. A robust to noise and clutter technique based on spatial density is also proposed to autonomously identify the targets location in the frame.",
keywords = "recursive density estimation, video streams processing, object detection and tracking",
author = "Pouria Sadeghi-Tehran and Plamen Angelov and Ramin Ramezani",
year = "2010",
month = jul,
doi = "10.1007/978-3-642-14058-7_4",
language = "English",
isbn = "978-3-642-14057-0",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "30--43",
editor = "H{\"u}llermeier, {Eyke } and Kruse, {Rudolf } and Hoffmann, {Frank }",
booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications",
note = "International Conference on Information Processing and Uncertainty Management, IPMU 2010 ; Conference date: 28-06-2010 Through 02-07-2010",
}
RIS
TY - GEN
T1 - A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties
AU - Sadeghi-Tehran, Pouria
AU - Angelov, Plamen
AU - Ramezani, Ramin
PY - 2010/7
Y1 - 2010/7
N2 - Real-time processing the information coming form video, infra-red or electro-optical sources is a challenging task due the uncertainties such as noise and clutter, but also due to the large dimensionalities of the problem and the demand for fast and efficient algorithms. This paper details an approach for automatic detection, single and multiple objects identification and tracking in video streams with applications to surveillance, security and autonomous systems. It is based on a method that provides recursive density estimation (RDE) using a Cauchy type of kernel. The main advantage of the RDE approach as compared to other traditional methods (e.g. KDE) is the low computational and memory storage cost since it works on a frame-by-frame basis; the lack of thresholds, and applicability to multiple objects identification and tracking. A robust to noise and clutter technique based on spatial density is also proposed to autonomously identify the targets location in the frame.
AB - Real-time processing the information coming form video, infra-red or electro-optical sources is a challenging task due the uncertainties such as noise and clutter, but also due to the large dimensionalities of the problem and the demand for fast and efficient algorithms. This paper details an approach for automatic detection, single and multiple objects identification and tracking in video streams with applications to surveillance, security and autonomous systems. It is based on a method that provides recursive density estimation (RDE) using a Cauchy type of kernel. The main advantage of the RDE approach as compared to other traditional methods (e.g. KDE) is the low computational and memory storage cost since it works on a frame-by-frame basis; the lack of thresholds, and applicability to multiple objects identification and tracking. A robust to noise and clutter technique based on spatial density is also proposed to autonomously identify the targets location in the frame.
KW - recursive density estimation
KW - video streams processing
KW - object detection and tracking
U2 - 10.1007/978-3-642-14058-7_4
DO - 10.1007/978-3-642-14058-7_4
M3 - Conference contribution/Paper
SN - 978-3-642-14057-0
T3 - Communications in Computer and Information Science
SP - 30
EP - 43
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
A2 - Hüllermeier, Eyke
A2 - Kruse, Rudolf
A2 - Hoffmann, Frank
PB - Springer
CY - Berlin
T2 - International Conference on Information Processing and Uncertainty Management, IPMU 2010
Y2 - 28 June 2010 through 2 July 2010
ER -
