W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Object Classification for Automated Surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Self-Calibration of a Camera from Video of a Walking Human
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Real-Time System for Classification of Moving Objects
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Human Detection using Geometrical Pixel Value Structures
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Machine Vision and Applications
Real-time pedestrian and vehicle detection in video using 3D cues
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Detection of user-defined, semantically high-level, composite events, and retrieval of event queries
Multimedia Tools and Applications
Vs-star: A visual interpretation system for visual surveillance
Pattern Recognition Letters
Active learning for transferrable object classification in cross-view traffic scene surveillance
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
| Hi-index | 0.00 |
In this paper, we present an object classification system for digital video surveillance which can be used for an arbitrary camera viewpoint. The system was designed to distinguish humans from vehicles for an arbitrary scene. The system employs a two phase approach. In the first phase, human/vehicle recognition is performed using classical feature-based classification. This phase is used to initialize view-normalization parameters. The parameters allow the second phase, to perform improved classification based on normalized features. The normalization also enables absolute identification of size and speed which can be used in various ways including identifying vehicles of a certain size and searching for objects traveling at specific speeds across different locations in the image and across different viewpoints/cameras.