The invariant features-based target tracking across multiple cameras

Target tracking across lenses is a popular research topic for video surveillance recently. This paper presents a method of target tracking across lenses with overlap regions. First, the target detection and tracking are completed with a single camera. Second, in order to obtain the location-invariant feature of the same target in the images with various cameras, the camera calibration is completed based on a three-dimension (3D) model. After that, for all images via multiple cameras, the coordinates of the 3D model are unified. Finally, referring to the assumption of spatial and temporal consistency of the target location across multiple cameras, the association among detected objects for the same target with different cameras is established. And a feature pool is built which contains perspective and scale features. Thus the same target is continuously tracked across multiple lenses. At last, the performance of the proposed approach is compared with KSP and PABC and demonstrated with indoor and outdoor experiments.

[1]  Jong Hyuk Park,et al.  Inference topology of distributed camera networks with multiple cameras , 2012, Multimedia Tools and Applications.

[2]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  David A. Forsyth,et al.  A novel algorithm for color constancy , 1990, International Journal of Computer Vision.

[4]  Mubarak Shah,et al.  Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views , 2008, Comput. Vis. Image Underst..

[5]  Yi-Ping Hung,et al.  An adaptive learning method for target tracking across multiple cameras , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.

[7]  Shaogang Gong,et al.  Multi-camera Matching using Bi-Directional Cumulative Brightness Transfer Functions , 2008, BMVC.

[8]  Mongi A. Abidi,et al.  Camera handoff with adaptive resource management for multi-camera multi-object tracking , 2010, Image Vis. Comput..

[9]  Gérard G. Medioni,et al.  Continuous tracking within and across camera streams , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Jean Orejarena.  A. Xolocotzi, Ricardo Gibu, Fenomenología del cuerpo y hermenéutica de la corporeidad , 2016 .

[11]  Simone Calderara,et al.  Bayesian-Competitive Consistent Labeling for People Surveillance , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Andrew Gilbert,et al.  Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity , 2006, ECCV.

[13]  Mubarak Shah,et al.  Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Mark S. Nixon,et al.  Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras , 2014, Multimedia Tools and Applications.

[16]  Mubarak Shah,et al.  Appearance modeling for tracking in multiple non-overlapping cameras , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Jian-Huang Lai,et al.  Spatial-temporal consistent labeling of tracked pedestrians across non-overlapping camera views , 2011, Pattern Recognit..

[18]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[19]  Christopher O. Jaynes,et al.  Object matching in disjoint cameras using a color transfer approach , 2007, Machine Vision and Applications.

[20]  Lily Lee,et al.  Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Ramesh C. Jain,et al.  An architecture for multiple perspective interactive video , 1995, MULTIMEDIA '95.

[22]  Tieniu Tan,et al.  Principal axis-based correspondence between multiple cameras for people tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[24]  Joost van de Weijer,et al.  Generalized Gamut Mapping using Image Derivative Structures for Color Constancy , 2008, International Journal of Computer Vision.

[25]  Simone Calderara,et al.  Consistent Labeling for Multi-camera Object Tracking , 2005, ICIAP.

[26]  Tieniu Tan,et al.  Object tracking across non-overlapping views by learning inter-camera transfer models , 2014, Pattern Recognit..