Estimating Motion and size of moving non-line-of-sight objects in cluttered environments

We present a technique for motion and size estimation of non-line-of-sight (NLOS) moving objects in cluttered environments using a time of flight camera and multipath analysis. We exploit relative times of arrival after reflection from a grid of points on a diffuse surface and create a virtual phased-array. By subtracting space-time impulse responses for successive frames, we separate responses of NLOS moving objects from those resulting from the cluttered environment. After reconstructing the line-of-sight scene geometry, we analyze the space of wavefronts using the phased array and solve a constrained least squares problem to recover the NLOS target location. Importantly, we can recover target's motion vector even in presence of uncalibrated time and pose bias common in time of flight systems. In addition, we compute the upper bound on the size of the target by backprojecting the extremas of the time profiles. Ability to track targets inside rooms despite opaque occluders and multipath responses has numerous applications in search and rescue, medicine and defense. We show centimeter accurate results by making appropriate modifications to a time of flight system.

[1]  Yasuyuki Matsushita,et al.  Shape from Second-Bounce of Light Transport , 2010, ECCV.

[2]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[4]  Ramesh Raskar,et al.  Fast separation of direct and global components of a scene using high frequency illumination , 2006, ACM Trans. Graph..

[5]  Kiriakos N. Kutulakos,et al.  A theory of inverse light transport , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[6]  J. Busck,et al.  Gated viewing and high-accuracy three-dimensional laser radar. , 2004, Applied optics.

[7]  Steve Marschner,et al.  Dual photography , 2005, ACM Trans. Graph..

[8]  Richard P. Hodges Underwater Acoustics: Analysis, Design and Performance of Sonar , 2010 .

[9]  Yan Huang,et al.  Tracking multiple objects through occlusions , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  David A. Forsyth,et al.  Combining Cues: Shape from Shading and Texture , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  D. J. Salmond,et al.  A particle filter for track-before-detect , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[14]  James Davis,et al.  5d time-light transport matrix: What can we reason about scene properties? , 2008 .

[15]  Benesty,et al.  Adaptive eigenvalue decomposition algorithm for passive acoustic source localization , 2000, The Journal of the Acoustical Society of America.

[16]  Neil D. Lawrence,et al.  WiFi-SLAM Using Gaussian Process Latent Variable Models , 2007, IJCAI.

[17]  G. Carter,et al.  The generalized correlation method for estimation of time delay , 1976 .

[18]  Ramesh Raskar,et al.  Looking around the corner using transient imaging , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[20]  T. Fukuchi,et al.  Laser Remote Sensing , 2005 .

[21]  S. Thrun,et al.  Monte carlo em for data-association and its applications in computer vision , 2001 .

[22]  E. Ng Is thermal scanner losing its bite in mass screening of fever due to SARS? , 2004, Medical physics.

[23]  Joaquin Quiñonero Candela,et al.  Local distance preservation in the GP-LVM through back constraints , 2006, ICML.

[24]  David J. Kriegman,et al.  Synthetic Aperture Tracking: Tracking through Occlusions , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[25]  Robert J. Mailloux,et al.  Phased Array Antenna Handbook , 1993 .

[26]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.