Multi-Layer People Detection using 2 D Range Data

This paper addresses the problem of detecting people using multiple layers of 2D range scans. Detecting persons is an important capacity for intelligent systems that have to interact with people. Our approach uses a supervised learning algorithm to train one classifier for each layer, which concentrates in a different body part. The classifiers are then combined in a probabilistic way to create a final robust detector. Experimental results with real data demonstrate the effectiveness of our approach to detect persons in cluttered environments, and its ability to deal with occlusions.

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