Object recognition using oriented model points
Computer Vision, Graphics, and Image Processing
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
New methods for matching 3-D objects with single perspective views
IEEE Transactions on Pattern Analysis and Machine Intelligence
The combinatorics of object recognition in cluttered environments using constrained search
Artificial Intelligence
Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
The background primal sketch: an approach for tracking moving objects
Machine Vision and Applications
Performance of optical flow techniques
International Journal of Computer Vision
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Background Model for Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Motion detection with nonstationary background
Machine Vision and Applications
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
FPGA-based Road Traffic Videodetector
DSD '07 Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools
Robust abandoned object detection using dual foregrounds
EURASIP Journal on Advances in Signal Processing
A Robust Video-Based Algorithm for Detecting Snow Movement in Traffic Scenes
Journal of Signal Processing Systems
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Computer Vision and Image Understanding
Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation
IEEE Transactions on Intelligent Transportation Systems
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
Moving Objects Detection Method Based on Brightness Distortion and Chromaticity Distortion
IEEE Transactions on Consumer Electronics
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient image gradient based vehicle localization
IEEE Transactions on Image Processing
Journal of Signal Processing Systems
| Hi-index | 0.00 |
This paper describes the FPGA-based hardware implementation of an algorithm for an automatic traffic surveillance sensor network. The aim of the algorithm is to extract moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed, using low power hardware of a sensor network node. A single, stationary, monochrome camera is used, mounted at a location high above the road. Occlusions are not detected, however simple shadow and highlight elimination is performed. The algorithm is designed for frame-rate efficiency and is specially suited for pipelined hardware implementation. The authors, apart from the careful selection of particular steps of the algorithm and the modifications towards parallel implementation, also proposed novel improvements such as backgrounds' binary mask combination or non-linear functions in highlight detection, resulting in increasing the robustness and efficiency of hardware realization. The algorithm has been implemented in FPGA and tested on real-time video streams from an outdoor camera.