Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
An image mosaicing module for wide-area surveillance
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
DAVID: discriminant analysis for verification of monuments in image data
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Engineering Applications of Artificial Intelligence
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
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In this paper, we propose an efficient multi-object recognition scheme for surveillance based on interest points of objects and their feature descriptors. In this scheme, we first define a set of object types of interest and collect their sample images. For each sample image, we detect interest points and construct their feature descriptors using SURF. Next, we perform a statistical analysis of the local features to select representative points among them. Intuitively, the representative points of an object are the interest points that best characterize the object. Finally, we calculate thresholds of each object for object recognition. User query is processed in a similar way. A given query image's local feature descriptors are extracted and then compared with the representative points of objects in the database. Especially, to reduce the number of comparisons required, we propose a method for merging descriptors of similar representative points into a single descriptor. This descriptor is different from typical SURF descriptor in that each element represents not a single value but a range. By using this merged descriptor, we can calculate the similarity between input image descriptor and multiple descriptors in database efficiently. In addition, since our scheme treats all the objects independently, it can recognize multiple objects simultaneously.