Detection and Segmentation of Multiple,Partially Occluded Objects by Grouping,Merging, Assigning Part Detection Responses |
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Authors: | Bo Wu Ram Nevatia |
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Affiliation: | (1) Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA 90089-0273, USA |
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Abstract: | We propose a method that detects and segments multiple, partially occluded objects in images. A part hierarchy is defined
for the object class. Both the segmentation and detection tasks are formulated as binary classification problem. A whole-object
segmentor and several part detectors are learned by boosting local shape feature based weak classifiers. Given a new image,
the part detectors are applied to obtain a number of part responses. All the edge pixels in the image that positively contribute
to the part responses are extracted. A joint likelihood of multiple objects is defined based on the part detection responses
and the object edges. Computation of the joint likelihood includes an inter-object occlusion reasoning that is based on the
object silhouettes extracted with the whole-object segmentor. By maximizing the joint likelihood, part detection responses
are grouped, merged, and assigned to multiple object hypotheses. The proposed approach is demonstrated with the class of pedestrians.
The experimental results show that our method outperforms the previous ones. |
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Keywords: | Object detection Object segmentation |
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