Title
Multiple Vehicle Tracking In Surveillance Videos
Abstract
In this paper, we present KNIGHT, a Windows-based stand-alone object detection, tracking and classification software, which is built upon Microsoft Windows technologies. The object detection component assumes stationary background settings and models background pixel values using Mixture of Gaussians. Gradient-based background subtraction is used to handle scenarios of sudden illumination change. Connected-component algorithm is applied to detected foreground pixels for finding object-level moving blobs. The foreground objects are further tracked based on a pixel-voting technique with the occlusion and entry/exit reasonings. Motion correspondences are established using the color, size, spatial and motion information of objects. We have proposed a texture-based descriptor to classify moving objects into two groups: vehicles and persons. In this component, feature descriptors are computed from image patches, which are partitioned by concentric squares. SVM is used to build the object classifier. The system has been used in the VACE-CLEAR evaluation forum for the vehicle tracking task. Corresponding system performance is presented in this paper. © Springer-Verlag Berlin Heidelberg 2007.
Publication Date
12-1-2007
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
4122 LNCS
Number of Pages
200-208
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
38049182834 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/38049182834
STARS Citation
Yun, Zhai; Berkowitz, Phillip; Miller, Andrew; Shafique, Khurram; and Vartak, Aniket, "Multiple Vehicle Tracking In Surveillance Videos" (2007). Scopus Export 2000s. 6235.
https://stars.library.ucf.edu/scopus2000/6235