Abstract
The background subtraction is a widely used approach for Detecting Moving Objects (DMO) by a static camera using a simple algorithm; however, it is very sensitive to the local gradual changes of illumination, shadows, non-rigid moving objects and partial or full target occlusion. In order to overcome these issues, and to bring more potential to the solution, we propose an effective method for object detection based on active contour with camera motion compensation. The principle of active contours is to evolve an initial curve towards the object of interest that corresponds to the boundaries of the moving objects. Once the object has been detected, then it can be tracked by the Kalman filter. This latter requires many preassumptions about models and noise characteristics. As an alternative, a new method based on the Smooth Variable Structure Filter (SVSF) is implemented. The SVSF was introduced in an effort to provide a more robust estimation strategy. Detection and tracking algorithms require stable images to recognize a real moving target position. Therefore, the captured image from cameras which are placed on the moving platform, have undesired jitters, shakes and blurs. Video stabilization becomes an indispensable technique which is focusing on removing unnecessary camera vibrations from image sequences using a homographic matrix by extracting features using FAST corner detector and FREAK feature descriptor. The proposed algorithm is validated in real-world and the obtained results confirm the efficiency and robustness of our approach.
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Akli, B.M., Abdelkrim, N., Fatima, H., Fethi, D. (2021). Moving Objects Detection and Tracking with Camera Motion Compensation. In: Bououden, S., Chadli, M., Ziani, S., Zelinka, I. (eds) Proceedings of the 4th International Conference on Electrical Engineering and Control Applications. ICEECA 2019. Lecture Notes in Electrical Engineering, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-15-6403-1_84
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DOI: https://doi.org/10.1007/978-981-15-6403-1_84
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