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30 October 2009 An integrated bottom-up and top-down computing process for car parsing
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749623 (2009) https://doi.org/10.1117/12.832663
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
This paper presents an integrated bottom-up and top-down computing process for parsing cars. By parsing, it means detecting all instances in an input image, and aligning their constituent parts, if appeared. The output of parsing is to construct configurations of car instances. In real scenarios such as in street scenes, cars often appear with different degree of occlusions, which bring two problems in car parsing: (1) Occlusions often fail those holistic methods, so we use a deformable part-based model. In terms of generative models, this paper proposed a star-like pictorial structural model based on the active basis model. The presented model is hierarchical and deformable. (2) In turn, part-based models entail integrated bottom-up and top-down computing processes. Bottom-up processes generated hypotheses from input images for each node in the deformable model. Top-down processes are followed to verify those bottom-up hypotheses in terms of their configurations. In order to evaluate the proposed method, we build up a dataset in which different kinds of occlusions are randomly added to cars. Experiment results show that the integrated bottom-up and topdown process improves the performance greatly.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiong Yang, Tianfu Wu, and Nong Sang "An integrated bottom-up and top-down computing process for car parsing", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749623 (30 October 2009); https://doi.org/10.1117/12.832663
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