The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Counting Crowded Moving Objects
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.
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Cites
Irani, M., & Anandan, P. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_18 · Cited by 250
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Crowd Analysis Using Computer Vision Techniques
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Clustering method for counting passengers getting in a bus with single camera
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Metadata
- Authors
- Rabaud, V., & Belongie, S.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 705-711
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2161841955
- Cited by
- 410
- References
- 1
- FWCI
- 14.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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