Object Association and Identification in Heterogeneous Sensors Environment

An approach for dynamic object association and identification is proposed for heterogeneous sensor network consisting of visual and identification sensors. Visual sensors track objects by a 2D localization, and identification sensors (i.e., RFID system, fingerprint, or iris recognition system) are incorporated into the system for object identification. This paper illustrates the feasibility and effectiveness of information association between the position of objects estimated by visual sensors and their simultaneous registration of multiple objects. The proposed approach utilizes the object dynamics of entering and leaving the coverage of identification sensors, where the location information of identification sensors and objects is available. We investigate necessary association conditions using set operations where the sets are defined by the dynamics of the objects. The coverage of identification sensor is approximately modeled by the maximum sensing coverage for a simple association strategy. The effect of the discrepancy between the actual and the approximated coverage is addressed in terms of the association performance. We also present a coverage adjustment scheme using the object dynamics for the association stability. Finally, the proposed method is evaluated with a realistic scenario. The simulation results demonstrate the stability of the proposed method against nonideal phenomena such as false detection, false tracking, and inaccurate coverage model.

[1]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Andrea Cavallaro,et al.  Target Detection and Tracking With Heterogeneous Sensors , 2008, IEEE Journal of Selected Topics in Signal Processing.

[3]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[4]  Sridhar Lakshmanan,et al.  A motion and shape-based pedestrian detection algorithm , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[5]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Dieter Fox,et al.  Knowledge Compilation Properties of Trees-of-BDDs, Revisited , 2009, IJCAI.

[7]  Sangjin Hong,et al.  Passive sensor based dynamic object association method in wireless sensor networks , 2007, 2007 50th Midwest Symposium on Circuits and Systems.

[8]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Sascha Spors,et al.  Joint audio-video object localization and tracking , 2001 .

[10]  Christian Hirt Radio Frequency Identification - RFID , 2004 .

[11]  Mostafa H. Ammar,et al.  ASAP: A Camera Sensor Network for Situation Awareness , 2007, OPODIS.

[12]  Hyunwoo Kim,et al.  Real-time multiple people detection using skin color, motion and appearance information , 2004, RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759).

[13]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Vassilis Kostakos,et al.  rfid in pervasive computing: State-of-the-art and outlook , 2009, Pervasive Mob. Comput..

[15]  Wei-Yun Yau,et al.  A Bayesian framework for robust human detection and occlusion handling human shape model , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[16]  M. A. Grudin,et al.  On internal representations in face recognition systems , 2000, Pattern Recognit..

[17]  Sangjin Hong,et al.  Passive sensor based dynamic object association with particle filtering , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[18]  Georgios Tziritas,et al.  Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis , 1999, IEEE Trans. Multim..

[19]  Sangjin Hong,et al.  Local Initiation Method for Multiple Object Association in Surveillance Environment with Multiple Cameras , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[20]  Wei-Yun Yau,et al.  A Bayesian Framework for Robust Human Detection and Occlusion Handling using Human Shape Model , 2004, International Conference on Pattern Recognition.

[21]  Kyoung-Su Park,et al.  Iterative Object Localization Algorithm Using Visual Images with a Reference Coordinate , 2008, EURASIP J. Image Video Process..

[22]  Kay Römer,et al.  Smart identification frameworks for ubiquitous computing applications , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..