||Improving UAV Ground Object Detection with Application of Machine Learning Mechanism
||Institute of Civil Aviation
The present main image surveillance methods can be divided into two categories, aerial surveillance and ground surveillance. The disadvantages of ground surveillance are the smaller detection range, costing more time and environmental restriction. Aerial surveillance is more suitable for a much larger spatial area, and it has higher flexibility for complex environments. This aerial surveillance system is mainly based on the machine learning algorithm to detect the objects which on the ground. The process of proposed system are divided into two parts, training process and testing process. In system preparation, the most important part of object detection is sample collection. The system uses the common and obvious parts which in the vehicle behind as the the main characteristics, such as wind screen, wiper and lamps. The collected samples are trained by using diverse train cascaded Haar-like feature classifier and LBP feature classifier in this thesis. According the final experiment result, it can improve effectively the detection rate, false alarm rate and process speed after the image processing. In this thesis, three experiments are conducted, including the data changes at different time, different altitude and different attitude.
The final experiment results show the proposed detection system can run effectively on a dynamic platform all day with constant altitude to detect different vehicles. The proposed detection system also can be applied on the UAVs or other flying devices in constant altitude or constant direction, in order to be developed into further application, such as real-time surveillance.
Keywords: Vehicle detection, Gray level, Blurring, Median filter, Machine learning, Haar-like feature, LBP feature, AdaBoost Algorithm, Cascading Classifier.
List of Figures.............VIII
List of Tables.............X
Chapter 1 Introduction.............1
1.2 Literature Survey.............2
1.3 Main Idea.............6
1.5 Thesis Outline.............7
Chapter 2 Images and Features in Processing.............9
2.1 Feature sample collection.............9
2.2 Image processing.............14
2.2.1 Gray Level.............14
2.2.2 Blurring and Median Filter.............17
2.2.3 Histogram Equalization.............19
Chapter 3 Vehicle Detection System Frame.............22
3.1.1 Haar-like feature.............23
3.1.2 Local Binary Patterns feature.............24
3.2 Boosting and Weak Classifier.............28
3.3 AdaBoost Algorithm.............29
3.4 Cascading Classifier.............35
3.5 System Structure.............37
Chapter 4 Experiment Result.............39
4.1 Experiment and Environment Set up.............39
4.2 Samples Training.............40
4.3 Video in vehicle detection experiments.............42
4.3.1 Fixed point at different time.............43
4.3.2 Fixed point at different height.............61
4.3.3 Dynamic experiment.............66
4.4 Merging two Machine Learning Algorithms.............76
Chapter 5 Conclusion and Future work.............79
 J. Y. Choi, Y. K. Yang, “Vehicle detection from aerial images using local shape information”, Advances in Image and Video Technology, Vol. 5414, January 2009, pp. 227-236.
 L. Eikvil, L. Aurdal and H. Koren, “Classification-Based Vehicle Detection in High-Resolution Satellite Images”, ISPRS J. Photogrammetry and Remote Sensing, Vol. 64, No. 1, pp. 65-72, 2009.
 H. Grabner, T. Nguyen, B. Gruber and H. Bischof, “On-Line Boosting-Based Car Detection from Aerial Images”, ISPRS J. Photogrammetry and Remote Sensing, Vol. 63, No. 3, pp. 382-396, 2008.
 S. Hinz, A. Baumgartner, “Vehicle detection in aerial images using generic features, grouping, and context”, Proceedings of 23rd DAGM Symposium Munich, Vol. 2191, September 2001, pp. 45-52.
 S. Hinz, “Detection and Counting of Cars in Aerial Images”, Proc. International Conference on Image Processing, 2003.
 X. Shi, H. Ling, E. Blasch, W. Hu, “Context-driven moving vehicle detection in wide area motion imagery”, 21st IEEE International Conference on Pattern Recognition (ICPR), November 2012, pp. 2512-2515.
 J. Leitloff, S. Hinz and U. Stilla, "Vehicle detection in very high resolution satellite images of city areas", IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 7, pp. 2795-2806, 2010.
 S. Sahli, Y. Ouyang, D. A. Lavigne, “Robust vehicle detection in low-resolution aerial imagery”, International Society for Optics and Photonics, Vol. 7668, April 2010, pp. 76680G-76680G.
 X. Cao, C. Wu, P. Yan and X. Li, "Linear SVM classification using boosting hog features for vehicle detection in low-altitude airborne videos", Image Processing (ICIP), 2011 18th IEEE International Conference, pp. 2421-2424.
 R. E. Schapire, "The boosting approach to machine learning: An overview." Nonlinear estimation and classification. Springer New York, 2003. pp. 149-171.
 R. Lienhart, J. Maydt, “An extended set of haar-like features for rapid object detection”, 2002 International Conference on Image Processing, Vol. 1, 2002, pp. I-900 - I-903.
 T. P. Breckon, S. E. Barnes, M. L. Eichner, K. Wahren, “Autonomous real-time vehicle detection from a medium-level UAV”, 24th International Conference on Unmanned Air Vehicle Systems, 2009, pp. 29.1-29.9.
 T. Ojala, M. Pietikäinen, T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, July 2002, pp. 971-987.
 Y. Freund, R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of computer and system sciences, vol. 55, December 1997, pp. 119-139.
 T. Ojala, M. Pietikäinen, H. David, "A comparative study of texture measures with classification based on featured distributions." Pattern recognition January 1996, pp. 51-59.
 A. Kembhavi, D. Harwood, L. S. Davis, "Vehicle detection using partial least squares." IEEE Transactions on Pattern Analysis and Machine Intelligence June 2011, pp. 1250-1265.
 C. H. Yung, C. C. Weng, Y. Y. Chen, "Vehicle detection in aerial surveillance using dynamic Bayesian networks." IEEE transactions on image processing April 2012, pp. 2152-2159.
 R. Chellappa, G. Qian, Q. Zheng, "Vehicle detection and tracking using acoustic and video sensors." Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP'04). IEEE International Conference on. Vol. 3. IEEE, 2004.
 C. C. Wang, S. S. Huang, L. C. Fu, "Driver assistance system for lane detection and vehicle recognition with night vision." 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005.
 Z. Sun, G. Bebis, and R. Miller. "On-road vehicle detection: A review." IEEE transactions on pattern analysis and machine intelligence May 2006, pp. 694-711.
 C. C. Wang, C. Thorpe, S. Thrun, "Online simultaneous localization and mapping with detection and tracking of moving objects: Theory and results from a ground vehicle in crowded urban areas." Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on. Vol. 1. IEEE, 2003.
 S. Gupte, O. Masoud, R. F. K. Martin, N. P. Papanikolopoulos, "Detection and classification of vehicles." IEEE Transactions on intelligent transportation systems January 2002, pp. 37-47.
 D. M. Gavrila, "Pedestrian detection from a moving vehicle." European conference on computer vision. Springer Berlin Heidelberg, 2000.
 W. W. Wierwille, S. S. Wreggit, C. L Kirn, L. A. Ellsworth, “Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness.” final report. No. HS-808 247. 1994.
 R. Labayrade, D. Aubert, J. P. Tarel, "Real time obstacle detection in stereovision on non flat road geometry through" v-disparity" representation." Intelligent Vehicle Symposium, 2002. IEEE. Vol. 2. IEEE, 2002.
 R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, R. Bowers, M. Boonstra, V. Korzhova, J. Zhang, "Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol." IEEE Transactions on Pattern Analysis and Machine Intelligence February 2009. pp. 319-336.
 D. M. Gavrila, S. Munder, "Multi-cue pedestrian detection and tracking from a moving vehicle." International journal of computer vision January 2007. pp. 41-59.