Comparison of target detection algorithms using adaptive background models

Authors:
D. Hall;J. Nascimento;P. Ribeiro;E. Andrade;P. Moreno;S. Pesnel;T. List;R. Emonet;R. B. Fisher;J. S. Victor;J. L. Crowley
Affiliations:
INRIA Rhone-Alpes, France;Digital Imaging Res. Centre, Kingston Univ., UK;Digital Imaging Res. Centre, Kingston Univ., UK;Dept. of Comput. Sci. & Eng., California Univ., San Diego, La Jolla, CA, USA;-;-;-;-;-;-;-
Venue:
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Year:
2005

Citing 0
Cited 13

Texture Dissimilarity Measures for Background Change Detection

ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Multi-layer Background Change Detection Based on Spatiotemporal Texture Projections

ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
The use of vanishing point for the classification of reflections from foreground mask in videos

IEEE Transactions on Image Processing
Simultaneous background/foreground segmentation and contour smoothing with level set based partial differential equation for intelligent surveillance systems over network

International Journal of Web and Grid Services
A Novel Approach to Robust Background Subtraction

CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
People re-identification by spectral classification of silhouettes

Signal Processing
Detecting and recognizing abandoned objects in crowded environments

ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Discriminative tracking by metric learning

ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Background division, a suitable technique for moving object detection

CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Stochastic approximation for background modelling

Computer Vision and Image Understanding
A hierarchical feature fusion framework for adaptive visual tracking

Image and Vision Computing
Long term carefully learning for person detection application to intelligent surveillance system

Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Cognitive visual tracking and camera control

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.