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Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery

Mathew, Alex
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849

Abstract Details

2014, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical Engineering.
Object detection and tracking in imagery captured by aerial systems are becoming increasingly important in computer vision research. In aerial imagery, objects can appear in any orientation, varying sizes and in different lighting conditions. Due to the exponential growth in sensor technology, increasing size and resolution of aerial imagery are becoming a challenge for real-time computation. A rotation invariant feature extraction technique for detecting and tracking objects in aerial imagery is presented in this dissertation. Rotation invariance in the feature representation is addressed by considering concentric circular regions centered at visually salient locations of the object. The intensity distribution characteristics of the object region are used to represent an object effectively. A set of intensity-based features is derived from intensity histograms of the circular regions and they are inherently rotation invariant. An integral histogram computation approach is used to compute these features efficiently. To improve the representational strength of the feature set for rotation and illumination-invariant object detection, a gradient-based feature set is derived from normalized gradient orientation histograms of concentric regions. Rotation invariance is achieved by considering the magnitude of the Discrete Fourier Transform (DFT) of the gradient orientation histograms. A novel computational framework called Integral DFT is presented for fast and efficient extraction of gradient-based features in large imagery. A part-based model, which relies on a representation of an object as an aggregation of significant parts, using the gradient-based features is also presented in this dissertation. Integrating the features of significant parts gives robustness to partial occlusions and slight deformations, thus leading to a better object representation. The effectiveness of the intensity-based feature is demonstrated in tracking objects in Wide Area Motion Imagery (WAMI) data. The object detection capability of the gradient-based feature extraction technique is evaluated on three different types of targets in low altitude aerial imagery. It is observed that the speed of computation is several times faster than state-of-the-art methods while maintaining comparable detection accuracies. Research work is continuing to automatically identify significant parts of an object to build the part-based model. Another direction of this research is to use the gradient-based rotation invariant features for scene matching.
Vijayan Asari (Committee Chair)
Keigo Hirakawa (Committee Member)
Raul Ordonez (Committee Member)
Youssef Raffoul (Committee Member)
137 p.
Computer Engineering; Computer Science; Electrical Engineering; Engineering
Rotation invariant feature; Aerial object detection; Pattern recognition; Object detection; Object tracking; Integral DFT

Recommended Citations

Citations

  • Mathew, A. (2014). Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849

    APA Style (7th edition)

  • Mathew, Alex. Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery. 2014. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849.

    MLA Style (8th edition)

  • Mathew, Alex. "Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery." Doctoral dissertation, University of Dayton, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849

    Chicago Manual of Style (17th edition)

dayton1397662849
3,154
© 2014, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.