An object-based comparative methodology for motion detection based on the F-Measure
Computer Vision and Image Understanding
IEEE Transactions on Circuits and Systems for Video Technology
Adaptable Neural Networks for Objects' Tracking Re-initialization
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Object Contour Tracking Using Foreground and Background Distribution Matching
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Hierarchical image gathering technique for browsing surveillance camera images
Proceedings of the 2007 conference on Human interface: Part I
Computer Vision and Image Understanding
Multimedia Tools and Applications
Visual event recognition using decision trees
Multimedia Tools and Applications
Trajectory classification using switched dynamical hidden Markov models
IEEE Transactions on Image Processing
Modeling and assessing quality of information in multisensor multimedia monitoring systems
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Logic-based trajectory evaluation in videos
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Expert Systems with Applications: An International Journal
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
Early smoke detection in video using swaying and diffusion feature
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we propose novel methods to evaluate the performance of object detection algorithms in video sequences. This procedure allows us to highlight characteristics (e.g., region splitting or merging) which are specific of the method being used. The proposed framework compares the output of the algorithm with the ground truth and measures the differences according to objective metrics. In this way it is possible to perform a fair comparison among different methods, evaluating their strengths and weaknesses and allowing the user to perform a reliable choice of the best method for a specific application. We apply this methodology to segmentation algorithms recently proposed and describe their performance. These methods were evaluated in order to assess how well they can detect moving regions in an outdoor scene in fixed-camera situations