Generalizing the Lucas-Kanade algorithm for histogram-based tracking
Pattern Recognition Letters
An adaptive motion segmentation for automated video surveillance
EURASIP Journal on Advances in Signal Processing
Object motion detection using information theoretic spatio-temporal saliency
Pattern Recognition
A Shape and Energy Based Approach to Vertical People Separation in Video Surveillance
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Measures for the evaluation of segmentation methods used in model based people tracking methods
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Suitability of edge segment based moving object detection for real time video surveillance
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Human tracking: a state-of-art survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Pattern Recognition and Image Analysis
Automated vision tracking of project related entities
Advanced Engineering Informatics
Region-growing detection of moving objects in video sequences based on optical flow
Pattern Recognition and Image Analysis
A flexible edge matching technique for object detection in dynamic environment
Applied Intelligence
Public Space Behavior Modeling With Video and Sensor Analytics
Bell Labs Technical Journal
A statistical operator for detecting weak edges in low contrast images
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Robust contour tracking using a modified snake model in stereo image sequences
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
| Hi-index | 0.00 |
We propose a fast and robust approach to the detection and tracking of moving objects. Our method is based on using lines computed by a gradient-based optical flow and an edge detector. While it is known among researchers that gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for detecting and tracking objects using this feature. In our method, extracted edges by using optical flow and the edge detector are restored as lines, and background lines of the previous frame are subtracted. Contours of objects are obtained by using snakes to clustered lines. Detected objects are tracked, and each tracked object has a state for handling occlusion and interference. The experimental results on outdoor-scenes show fast and robust performance of our method. The computation time of our method is 0.089 s/frame on a 900 MHz processor.