Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
A Real-time Vision-based Vehicle Tracking and Traffic Surveillance
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Intelligent Vehicle Counting Method Based on Blob Analysis in Traffic Surveillance
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Automatic traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems
Audio-Visual Event Recognition in Surveillance Video Sequences
IEEE Transactions on Multimedia
Event detection in field sports video using audio-visual features and a support vector Machine
IEEE Transactions on Circuits and Systems for Video Technology
Coupled multi-object tracking and labeling for vehicle trajectory estimation and matching
Multimedia Tools and Applications
Dynamic background modeling for a safe road design
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
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In this paper, we combine rigid motion-based tracking algorithms and non-linear identification methods for automatic detecting and tracking vehicles' trajectory in roadways. In addition, we introduce the concept of the angle spectrum for determining the deviation of a vehicle trajectory from the ideal trace, provided by surveyor engineers. Motion-based tracking is implemented through frame differencing and advanced non-linear convolution filters such as the morphological opening by reconstruction. However, motion based tracking suffers from noise, occlusions and the fact that the detected moving region may contains more than one foreground objects (e.g., a vehicle approach another vehicle). For this reason, a neural network-based classification scheme is adopted in this paper for identifying foreground/background objects. The neural network models the colour and texture properties of the detected moving objects. Fusion algorithm are then exploited which it combine the output of the neural network classifier and the output of the motion-based tracking for efficiently detecting the vehicles trajectory. In the following, we introduce the concept of the angle spectrum which estimates the deviation between two curves, i.e., the vehicle trajectory and the ideal trace. The angle spectrum is computed through quantization of the polar coordinate space, adopted for the curve representation along with novel matching schemes. Experimental results are presented, which indicate the performance of the proposed method in real file environments.