Conference Paper

Cross-Articulation Learning for Robust Detection of Pedestrians.

DOI: 10.1007/11861898_25 Conference: Pattern Recognition, 28th DAGM Symposium, Berlin, Germany, September 12-14, 2006, Proceedings
Source: DBLP

ABSTRACT Recognizing categories of articulated objects in real-world scenarios is a challenging problem for today's vision algorithms. Due to the large appearance changes and intra-class variability of these objects, it is hard to define a model, which is both general and discriminative enough to capture the properties of the category. In this work, we pro- pose an approach, which aims for a suitable trade-off for this problem. On the one hand, the approach is made more discriminant by explic- itly distinguishing typical object shapes. On the other hand, the method generalizes well and requires relatively few training samples by cross- articulation learning. The effectiveness of the approach is shown and compared to previous approaches on two datasets containing pedestri- ans with different articulations.

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