Paper Info
Title
Collaborative Sparse Approximation for Multiple-Shot Across-Camera Person Re-identification
Abstract
In this paper we propose a simple and effective solution to the important and challenging problem of across-camera person re-identification. We focus on the common case in video surveillance where multiple images or video frames are available for each person. Instead of exploring new features, the proposed approach aims at making a better use of such images/frames. It builds a collaborative representation over all the gallery images (of known person individuals) to best approximate the query images (containing an unknown person) via affine combinations. The approximation is measured by the nearest point distance between the two affine hulls constructed by the query images and gallery images, respectively. By enforcing the sparsity of the samples used for approximating the two nearest points, the relative importance of the gallery images belonging to different persons has the ability to reveal the identity of the querying person. Extensive experiments on public benchmark datasets demonstrate that the proposed approach greatly outperforms the state-of-the-art methods.
Year
DOI
Venue
2012
10.1109/AVSS.2012.21
AVSS
Keywords
Field
DocType
known person individual,query image,multiple-shot across-camera person re-identification,unknown person,querying person,different person,across-camera person re-identification,gallery image,collaborative sparse approximation,nearest point,affine combination,collaboration,image sensors,sparse representation,face recognition,benchmark testing
Affine transformation,Computer vision,Facial recognition system,Image sensor,Pattern recognition,Computer science,Sparse approximation,Camera network,Artificial intelligence,Benchmark (computing)
Conference
Citations 
PageRank 
References 
16
0.56
15
Authors
5
Name
Order
Citations
PageRank
Yang Wu11045.48
Michihiko Minoh234958.69
Masayuki Mukunoki319921.86
Wei Li433228.56
Shihong Lao52005118.22