Small Moving Object Detection Based on Sequence Confidence Method in UAV Video
In the small object detection on the UAV (Unmanned Aerial Vehicle) platform, the confidence description of the moving object is proposed to improve the accuracy, robustness and reliable tracking method of the object detection. Due to the low resolution and slow motion of small moving object in aerial video, and the image is easily subject to illumination and camera jitter noise, and the correlation between video sequences is neglected, it is prone to false detection of moving object and low detection accuracy, the characteristics of poor robustness. For the UAV video with small moving object, the algorithm uses the ORB operator to extract reliable global feature points for each frame of the video, and then performs global motion compensation on the motion background through the affine transformation model and calculates the difference image. The energy accurately detects the small object, and then describes the confidence of the moving object. The n-step back-off method is used to increase the correlation information between the video sequences. The proposed method is to evaluate the video captured on the airborne aircraft, and has done a lot of experiments and tests. For the object as small as 25 pixels, the method still has better performance, and our method can be realized by parallel computing. Real-time, processing 1280 × 720 frames at around 45 fps.
KeywordsUAV Aerial video Sequence confidence Small object detection
This work was supported by National Nature Science Foundation of China (NSFC) under Grants 61671356, 61201290.
- 1.Annaka, K., Munakata, H., Kanamura, K.: Electrochemical evaluation of Li4Ti5O12 single particle at various temperatures. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 6(9), 1281–1295 (2012)Google Scholar
- 2.Andrew, A.M.: Multiple view geometry in computer vision. Kybernetes 30(9/10), 1865–1872 (2004)Google Scholar
- 5.Ludington, B., Reimann, J., Vachtsevanos, G.: Target tracking and adversarial reasoning for unmanned aerial vehicles. In: IEEE Aerospace Conference, pp. 1–17 (2007)Google Scholar
- 7.Bell, W., Felzenszwalb, P., Huttenlocher, D.: Detection and long term tracking of moving objects in aerial video (1999)Google Scholar
- 8.Kanade, T., Amidi, O., Ke, Q.: Real-time and 3D vision for autonomous small and micro air vehicles. In: IEEE Conference on Decision and Control (CDC), vol. 2, pp. 1655–1662. IEEE (2004)Google Scholar
- 9.Mondragon, I.F., Campoy, P., Correa, J.F., et al.: Visual model feature tracking for UAV control. In: IEEE International Symposium on Intelligent Signal Processing, pp. 1–6. IEEE (2008)Google Scholar
- 10.Ali, S., Shah, M.: COCOA: tracking in aerial imagery. Society of Photo-optical Instrumentation Engineers. International Society for Optics and Photonics, pp. 105–114Google Scholar
- 11.Ali, S., Reilly, V., Shah, M.: Motion and appearance contexts for tracking and re-acquiring targets in aerial videos. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–6. IEEE (2007)Google Scholar
- 12.Chung, Y.C., He, Z.: Low-complexity and reliable moving objects detection and tracking for aerial video surveillance with small UAVS. In: IEEE International Symposium on Circuits and Systems, pp. 2670–2673. IEEE (2007)Google Scholar
- 15.Rublee, E., Rabaud, V., Konolige, K., et al.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, pp. 2564–2571. IEEE (2012)Google Scholar