Identifying and segmenting human-motion for mobile robot navigation using alignment errors

This paper presents a new human-motion identification and segmentation algorithm, for mobile robot platforms. The algorithm is based on computing the alignment error between pairs of object images acquired from a moving platform. Pairs of images generating relatively small alignment errors are used to estimate the fundamental frequency of the object's motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object's motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects

[1]  Larry S. Davis,et al.  Probabilistic template based pedestrian detection in infrared videos , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[2]  Jeffrey E. Boyd Video Phase-Locked Loops in Gait Recognition , 2001, ICCV.

[3]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[4]  Yang Song,et al.  Unsupervised Learning of Human Motion , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  L. Davis,et al.  Iterative figure-ground discrimination , 2004, ICPR 2004.

[6]  Robert T. Collins,et al.  Silhouette-based human identification from body shape and gait , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[7]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  James J. Little,et al.  Motion from Transient Oscillations , 2001 .

[9]  Radu Horaud,et al.  Figure-Ground Discrimination: A Combinatorial Optimization Approach , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  M. Nixon,et al.  Global Statistical Description of Temporal Features , 2000 .

[11]  M. Teague Image analysis via the general theory of moments , 1980 .

[12]  Larry S. Davis,et al.  An Efficient and Robust Human Classification Algorithm using Finite Frequencies Probing , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[13]  Yanxi Liu,et al.  Gait Sequence Analysis Using Frieze Patterns , 2002, ECCV.

[14]  Mark S. Nixon,et al.  Zernike Velocity Moments for Description and Recognition of Moving Shapes , 2001, BMVC.

[15]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[16]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[17]  Michael Lindenbaum,et al.  Ground from Figure Discrimination , 1999, Comput. Vis. Image Underst..

[18]  J. Little,et al.  Recognizing People by Their Gait: The Shape of Motion , 1998 .

[19]  Dariu Gavrila,et al.  Sensor-Based Pedestrian Protection , 2001, IEEE Intell. Syst..