Abstract:

An accurate and computationally very fast multimodal human detector is presented. This 1D+2D detector fuses 1D range scan and 2D image information via an effective geometric descriptor and a silhouette based visual representation within a radial basis function kernel support vector machine learning framework. Unlike the existing approaches, the proposed 1D+2D detector does not make any restrictive assumptions on the range scan positions, thus it is applicable to a wide range of real-life detection tasks. To analyze the discriminative power of the geometric descriptor, a range scan only version, 1D+, is also evaluated. Extensive experiments demonstrate that the 1D+2D detector works robustly under challenging imaging conditions and achieves several orders of magnitude performance improvement while reducing the computational load drastically. In addition, a new multi-modal (LIDAR, depth image, optical image) dataset, DontHitMe, is introduced. This dataset contains 40,000 registered frames and 3,600 manually annotated human objects. It depicts challenging illumination conditions in indoors and outdoors environments and is publicly available to our community.