Pedestrian detection systems are finding their way into many modern ''intelligent'' vehicles. The body posture could reveal further insight about the pedestrian's intent and her awareness of the oncoming car. This article details the algorithms and implementation of a library for real-time body posture recognition. It requires prior person detection and then calculates overall stance, torso orientation in four increments, and head location and orientation, all based on individual frames. A syntactic post-processing module takes temporal information into account and smoothes the results over time while correcting improbable configurations. We show accuracy and timing measurements for the library and its utilization in a training application.
Abstract. This paper primarily investigates the possibility of using multi-level learning of sparse parts-based representations of US Marine postures in an outside and often crowded environment for training exercises. To do so, the paper discusses two approaches to learning partsbased representations for each posture needed. The first approach uses a two-level learning method which consists of simple clustering of interest patches extracted from a set of training images for each posture, in addition to learning the nonparametric spatial frequency distribution of the clusters that represents one posture type. The second approach uses a two-level learning method which involves convolving interest patches with filters and in addition performing joint boosting on the spatial locations of the first level of learned parts in order to create a global set of parts that the various postures share in representation. Experimental results on video from actual US Marine training exercises are included.
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