People re-identification by graph kernels methods
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
A graph-kernel method for re-identification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
(MP)2T: multiple people multiple parts tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Addressing the semantic gap between video sensors and applications
Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
Online parameter tuning for object tracking algorithms
Image and Vision Computing
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In this paper a system for autonomous video surveillance in relatively unconstrained environments is described. The system consists of two principal phases: object detection and object tracking. An adaptive background subtraction, together with a set of corrective algorithms, is used to cope with variable lighting, dynamic and articulate scenes, etc. The tracking algorithm is based on a matrix representation of the problem, and is used to face splitting and occlusion problems. When the tracking algorithm fails in following actual object trajectories, an appearancebased module is used to restore object identities. An experimental evaluation, carried out on the PETS2009 dataset for tracking, shows promising results.