Abstract
For the aging population, surveillance in household environments has become more and more important. In this paper, we present a household robot that can detect abnormal events by utilizing video and audio information. In our approach, moving targets can be detected by the robot using a passive acoustic location device. The robot then tracks the targets by employing a particle filter algorithm. To adapt to different lighting conditions, the target model is updated regularly based on an update mechanism. To ensure robust tracking, the robot detects abnormal human behavior by tracking the upper body of a person. For audio surveillance, Mel frequency cepstral coefficients (MFCC) is used to extract features from audio information. Those features are input to a support vector machine classifier for analysis. Experimental results show that the robot can detect abnormal behavior such as “falling down” and “running”. Also, a 88.17% accuracy rate is achieved in the detection of abnormal audio information like “crying”, “groan”, and “gun shooting”. To lower the false alarms by abnormal sound detection system, the passive acoustic location device directs the robot to the scene where abnormal events occur and the robot can employ its camera to further confirm the occurrence of the events. At last, the robot will send the image captured by the robot to the mobile phone of master.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22, 8 (2000)
Olson, T., Brill, F.: Moving object detection and event recognition algorithms for smart cameras. In: Proc. DARPA Image Understanding Workshop, pp. 159–175, New Orleans, May 1997
Zhao, T., Nevatia R.: Tracking multiple humans in complex situations. IEEE Trans. Pattern Anal. Mach. Intell. 26, 9 (2004)
Radhakrishnan, R., Divakaran, A.: Systematic acquisition of audio classes for elevator surveillance. In: Proc. of SPIE, pp. 64–71. Austin, 24–26 May 2005
Luo, R.C., Su, K.L.: A multiagent multisensor based real-time sensory control system for intelligent security robot. In: Proceedings of International Conference on Robotics and Automation, Taiwan, 14–19 September 2003
Massios, N., Voorbraak, F.: Hierarchical decision-theoretic planning for autonomous robotic surveillance Massios. In: Advanced Mobile Robots, 1999 Third European Workshop, pp. 219–226. Zurich, 6–8 September 1999
Wang, H., Suter, D., Schindler, K., Shen, C.: TAdaptive object tracking based on an effective appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 29, 9 (2007)
Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27, 11 (2005)
Carpenter, J., Clifford, P., Fernhead, P.: An improved particle filter for non-linear problems. Technical Report, Dept. Statistics, Univ. of Oxford (1997)
Arulampalam, M.S., Maskell, S., Gordon N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50, 2 (2002)
Scheirer, E., Slaney, M.: Construction and evaluation of a robust multifeature speech/music discriminator. In: Proc. of International Conference on Acoustics, Speech, and Signal Processing, pp. 1331–1334. Munich, 21–24 April 1997
Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)
Holmes, J.N., Holmes, W.J.: Speech Synthesis and Recognition, 2nd edn. Taylor & Francis CRC, London (2001)
Logan, B.T.: Mel frequency cepstral coefficients for music modeling. In: Proceedings of the First International Symposium on Music Information Retrieval, Bloomington, 15–17 October 2001
Foote, J.: An overview of audio information retrieval. Multimedia Syst. 7(1), 2–10 (1999)
Radhakrishnan, R., Divakaran, A., Smaragdis, P.: Audio analysis for surveillance applications. In: IEEE Workshop on Application of Signal Processing to Audio and Acoustics, pp. 158–161. New Paltz, 16–19 October 2005
Cristianini, N., Shawe-Taylor, J.: A Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
Bernhard, S., Burges, C.J.C., Smola, A.: Advanced in Kernel Methods Support Vector Learning. MIT, Cambridge (1998)
Ou, Y., Wu, X.Y., Qian, H.H., Xu, Y.S.: A real time race classification system. In: Information Acquisition, 2005 IEEE International Conference. Hong Kong, 27 June–3 July 2005
Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: European Conference on Computer Vision, pp. 661–675. Copenhagen, 27 May–2 June 2002
Ou, Y.S., Qian, H.H., Wu, X.Y., Xu, Y.S.: Real-time surveillance based on human behavior analysis. Int. J. Inf. Acquis. 2(4), 353–365 (December 2005)
Wu, X.Y., Qin, J.Z., Cheng, J., Xu, Y.S.: Detecting audio abnormal information. In: The 13th International Conference on Advanced Robotics, pp. 550–554. Jeju, 21–24 August 2007
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, X., Gong, H., Chen, P. et al. Surveillance Robot Utilizing Video and Audio Information. J Intell Robot Syst 55, 403–421 (2009). https://doi.org/10.1007/s10846-008-9297-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-008-9297-3