Title

Detecting And Segmenting Humans In Crowded Scenes

Keywords

Human detection; Object recognition; Segmentation

Abstract

We describe an approach for detecting and segmenting humans with extensive posture articulations in crowded video sequences. In our method we learn a set of mean posture clusters, and a codebook of local shape distributions for humans in various postures. Detection proceeds in two stages: first instances of the codebook entries cast votes for locations of humans in the video and their respective postures. Subsequently, consistent hypotheses are found as maxima within a voting space. The segmentation of humans in the scene is initialized by the corresponding posture clusters and contours are evolved to obtain precise and consistent segmentations. Our experimental results indicate that the framework provides a simple yet effective means for aggregating local and global shape-based cues. The proposed method is capable of detecting and segmenting humans in crowded scenes as they perform a diverse set of activities and undergo a wide range of articulations within different contexts. Copyright 2007 ACM.

Publication Date

12-1-2007

Publication Title

Proceedings of the ACM International Multimedia Conference and Exhibition

Number of Pages

353-356

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1291233.1291310

Socpus ID

37849044611 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/37849044611

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