Bayesian Models for Dynamic Scene Analysis

Csaba Benedek ()
Additional contact information
Csaba Benedek: Institute for Computer Science and Control (SZTAKI)

Chapter Chapter 3 in Multi-Level Bayesian Models for Environment Perception, 2022, pp 25-78 from Springer

Abstract: Abstract In this chapter, we discuss Bayesian approaches for foreground object detection and localization in video surveillance applications. Two different sensors are used for these tasks: conventional electro-optical video cameras, and Rotating Multi-Beam (RMB) LidarLight Detection and Ranging (Lidar) sensors. For the camera image sequences, we propose first a Markov Random Field (MRF)Markov Random Field (MRF)-based foreground extraction technique which is able to address cast shadow detectionShadow detection and the exploitation of spatial coherence of the color and texture values observed in the foreground regions. Thereafter, based on the extracted foreground masks, we present a new Marked Point Process (MPP)Marked Point Process (MPP)-based method for pedestrian localization and height estimation in multi-camera systems, and give a detailed comparative evaluation of the proposed method versus a state-of-the-art technique. The last part of the chapter deals with LidarLight Detection and Ranging (Lidar) point cloud processing where key challenges are compensating the low and inhomogeneous spatial resolution of the measurements, and various artifacts in point cloud formation caused by the rotating sensor technology. We also present here application examples including motionMotion detection detection, gait-based pedestrian re-identificationRe-identification and activity recognition using a single RMB LidarRotating Multi-beam Lidar (RMB Lidar) sensor which monitors the scene from a fixed position.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-83654-2_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030836542

DOI: 10.1007/978-3-030-83654-2_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().