Name
Abstract | ||
|---|---|---|
This paper presents a unified approach to crowd segmentation. A global solution is generated using an Expectation Maximization framework. Initially, a head and shoulder detector is used to nominate an exhaustive set of person locations and these form the person hypotheses. The image is then partitioned into a grid of small patches which are each assigned to one of the person hypotheses. A key idea of this paper is that while whole body monolithic person detectors can fail due to occlusion, a partial response to such a detector can be used to evaluate the likelihood of a single patch being assigned to a hypothesis. This captures local appearance information without having to learn specific appearance models. The likelihood of a pair of patches being assigned to a person hypothesis is evaluated based on low level image features such as uniform motion fields and color constancy. During the E-step, the single and pairwise likelihoods are used to compute a globally optimal set of assignments of patches to hypotheses. In the M-step, parameters which enforce global consistency of assignments are estimated. This can be viewed as a form of occlusion reasoning. The final assignment of patches to hypotheses constitutes a segmentation of the crowd. The resulting system provides a global solution that does not require background modeling and is robust with respect to clutter and partial occlusion. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88693-8_51 | COMPUTER VISION - ECCV 2008, PT IV, PROCEEDINGS |
Keywords | Field | DocType |
color constancy,global optimization,image features,expectation maximization | Computer vision,Color constancy,Pairwise comparison,Segmentation,Clutter,Feature (computer vision),Computer science,Expectation–maximization algorithm,Artificial intelligence,Detector,Machine learning,Grid | Conference |
Volume | ISSN | Citations |
5305 | 0302-9743 | 20 |
PageRank | References | Authors |
1.77 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Tu | 1 | 125 | 8.58 |
| Thomas Sebastian | 2 | 121 | 5.03 |
| Gianfranco Doretto | 3 | 1026 | 78.58 |
| Nils Krahnstoever | 4 | 334 | 21.73 |
| Jens Rittscher | 5 | 686 | 67.07 |
| Ting Yu | 6 | 301 | 20.77 |