Agenda. Presentation of ImageLab. Digital Library content-based retrieval. Computer Vision for robotic automation. Multimedia: video annotation. Medical Imaging. Video analysis for indoor/outdoor surveillance. Off-line Video analysis for telemetry a nd forensics.
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Multimedia: video annotation
Off-line Video analysis
People and vehicle surveillance
Lab of Computer Vision,
Pattern Recognition and Multimedia
Dipartimento di Ingegneria dell’Informazione
Università di Modena e Reggio Emilia Italy
Italian & Regional
AD-HOC: Appearance Driven Human tracking with Occlusion Handling
Imagelab videos (available on ViSOR)
Working in real time at 10 fps!
Distributed surveillancewith non overlapping field of view
A possiblepathbetweenCamera1 and Camera 4
Single camera tracking: Multicamera tracking
“VIP: Vision tool for comparing Images of People”
Lantagne & al., Vision Interface 2003
Each extracted silhouette is segmented into significant region using the JSEG algorithm
( Y. Deng ,B.S. Manjunath: “Unsupervised segmentation of color-texture regions in images and video” )
Colour and texture descriptors are calculated for each region
To compare the regions inside two silhouette, a region matching scheme is used,
involving a modified version of the IRM algorithm presented in J.Z. Wang et al, ”Simplicity:
Semantics-sensitive integrated matching for picture libraries” .
The IRM algorithm is simple and works as follows:
1) The first step is to calculate all of the similarities
between all regions.
2) Similarities are sorted in decreasing order, the
first one is selected, and areas of the
respective pair of regions are compared.
A weight, equal to the smallest percentage area
between the two regions, is assigned to the
3) Then, the percentage area of the largest region is updated by removing the
percentage area of the smallest region so that it can be matched again.
The smallest region will not be matched anymore with any other region.
4) The process continues in decreasing order for all of the similarities.
In the end the overall similarity between the two region sets is calculated as:
ViSOR: Video Surveillance Online Repository
A. Prati, I. Mikic, M.M. Trivedi, R. Cucchiara, "Detecting Moving Shadows: Algorithms and Evaluation" in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, n. 7, pp. 918-923, July, 2003
We need videos and annotations!
Dataset: "ActionsasSpace-TimeShapes (ICCV '05)." M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri
R. Vezzani, M. Piccardi, R. Cucchiara, "An efficientBayesianframeworkfor on-line actionrecognition" in press on Proceedingsof the IEEE International Conference on Image Processing, Cairo, Egypt, November 7-11, 2009