Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/662434
Title: Counting crowded moving objects
Authors: Rabaud, Vincent;Belongie, Serge
subject: Science & Technology
Year: 2008
Abstract: In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.
Description: 

URI: http://localhost/handle/Hannan/377476
http://localhost/handle/Hannan/662434
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL508320.pdf1.28 MBAdobe PDF
Title: Counting crowded moving objects
Authors: Rabaud, Vincent;Belongie, Serge
subject: Science & Technology
Year: 2008
Abstract: In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.
Description: 

URI: http://localhost/handle/Hannan/377476
http://localhost/handle/Hannan/662434
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL508320.pdf1.28 MBAdobe PDF
Title: Counting crowded moving objects
Authors: Rabaud, Vincent;Belongie, Serge
subject: Science & Technology
Year: 2008
Abstract: In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.
Description: 

URI: http://localhost/handle/Hannan/377476
http://localhost/handle/Hannan/662434
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL508320.pdf1.28 MBAdobe PDF