Rotation-invariant histogram features for threat object detection on pipeline right-of-way

We present a novel algorithm that automatically detects anomalies in pipeline right of the way (ROW) regions in aerial surveillance videos. Early detection of anomalies on pipeline ROWs avoids failures or leaks. Vehicles that are potential threats vary in size, shape and color. The detection algorithm should be fast to enable detection in real-time. In this paper, we propose a rotation-invariant gradient histogram based descriptor built in CIELab color space. An SVM with radial basis kernel is used as the classifier. We use only the a and b components since they represent the color values. The region of interest is divided into concentric circular regions. The number of such concentric regions is based on the size of the target. The inner regions capture the local characteristics and finer details of the image. Larger regions capture the global characteristics. A noise reducing differentiation kernel is used to compute the gradient of the region to cope with motion blur and noise introduced by atmospheric aberrations. A gradient orientation histogram is constructed in each region by voting the magnitude of gradients. The final descriptor is build by concatenating the magnitude of DFT of orientation histograms collected from a component and b components. The magnitude of Discrete Fourier Transform (DFT) of the histogram is invariant to rotations. DFT can be efficiently computed as Fast Fourier Transfrom (FFT). Since the algorithm uses a sliding window detector, it can easily be parallelized.