@article {13203, title = {Reliable Segmentation of Pedestrians in Moving Scenes}, journal = {The 2005 International Conference on Acoustics, Speech, and Signal Processing (ICASSP2005)}, year = {2005}, month = {2005///}, abstract = {This paper describes a periodic motion based pedestriansegmentation algorithm for videos acquired from moving platforms. Given a sequence of bounding boxes containing the detected and tracked walking human, the goal is to analyze the low dimension structure by considering every object sample as a point in the high dimensional manifold space and use the learned structure for segmentation. In this work, unlike the traditional top- down dimension reduction (manifold learning) methods such as Isomap and locally linear embedding (LLE) [9], we introduce a novel bottom-up learning approach. We represent the human stride as a cascade of models with increasing parameter numbers. These parameters describe the dynamics of pedestrians from coarse to fine. By applying the learned manifold structure, we can predict the location of body parts, especially legs, with high accuracy at every frame. The segmentation in consecutive images is done by EM clustering. With the accuracy for prediction using twin-pendulum model, EM is more likely to converge to global maximums. Experimental results for real videos are presented. The algorithm has demonstrated a reliable performance for videos acquired from moving platforms. }, author = {Ran, Y. and Zheng,Q. and Weiss, I. and Davis, Larry S.} }