An autonomous monitoring system based on object shape detection

• Autorzy: Deniziak S. , Michno T. , Pięta P.
• Języki publikacji: EN

Abstrakty

EN

In traditional monitoring systems, stationary cameras are supervised only by a human operator, who may easily miss some events recorded by a camera. Because it is imperative for a surveillance system to be reliable, its autonomy can be extended by applying computer vision algorithms to a video signal and also by the use of mobile robots capable of monitoring tight and occluded areas. In this paper, we present an overview of the concept of an autonomous monitoring system based on object shape detection. Our goal is to develop a real-time system which robustly and efficiently identifies objects on the basis of their approximate shape. For monitoring the environment we use active and smart cameras capable of remote position control, as well as mobots equipped with video sensors. After performing the object extraction from individual video frames, each new detected object is decomposed into simple graphical primitives like lines, circles, rectangles etc. and then identified in a database using the Query by Shape (QS) method.

Słowa kluczowe

EN

monitoring system; object identification; image retrieval; machine vision

• Wydawca: Wydawnictwo PAK
• Czasopismo: Measurement Automation Monitoring
• Rocznik: 2015
• Tom: Vol. 61, No. 7
• Strony: 349--351
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wzory

Twórcy

autor

Deniziak S.

• Kielce University of Technology, 7 Tysiaclecia Panstwa Polskiego Ave., 25-314 Kielce, Poland

autor

Michno T.

• Kielce University of Technology, 7 Tysiaclecia Panstwa Polskiego Ave., 25-314 Kielce, Poland

autor

Pięta P.

• Kielce University of Technology, 7 Tysiaclecia Panstwa Polskiego Ave., 25-314 Kielce, Poland

Bibliografia

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• [4] Chen T. P., Haussecker H., Bovyrin A., Belenov R., Rodyushkin K., Kuranov A., and Eruhimov V.: Computer Vision Workload Analysis: Case Study of Video Surveillance Systems. Intel Technology Journal, vol. 9, 2005.
• [5] Deniziak S., and Michno T.: Query by Shape for Image Retrieval from Multimedia Databases. Springer, Beyond Databases, Architectures and Structures, pp. 377–386. 2015.
• [6] Tajeripour F., Saberi M., Fekri-Ershad S.: Developing a Novel Approach for Content Based Image Retrieval Using Modified Local Binary Patterns and Morphological Transform, 2014.
• [7] Greenspan H., Dvir G., and Rubner Y.: Region Correspondence for Image Matching via EMD Flow. IEEE, IEEE Workshop on Content-based Access of Image and Video Libraries, pp. 27–31, 2000.
• [8] Destrempes F., Mignotte M., and Angers J.-F.: Localization of Shapes Using Statistical Models and Stochastic Optimization. IEEE, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 9, pp. 1603–1615, 2007.
• [9] Deniziak S., and Michno T.: Query-by-Shape Interface for Content Based Image Retrieval. 8th International Conference on Human System Interaction, pp. 108–114, 2015.