Sidike Paheding

Recent Progress in Wide-Area Surveillance: Protecting Our Pipeline Infrastructure

The pipeline industry has millions of miles of pipes buried along the length and breadth of the country. Since none of the areas through which pipelines run are to be used for other activities, it needs to be monitored so as to know whether the right-of- way (RoW) of the pipeline is encroached upon at any point in time. Rapid advances made in the area of sensor technology have enabled the use of high end video acquisition systems to monitor the RoW of pipelines. The images captured by aerial data acquisition systems are affected by a host of factors that include light sources, camera characteristics, geometric positions and environmental conditions. We present a multistage framework for the analysis of aerial imagery for automatic detection and identification of machinery threats along the pipeline RoW which would be capable of taking into account the constraints that come with aerial imagery such as low resolution, lower frame rate, large variations in illumination, motion blurs, etc. The proposed framework is described from three directions. In the first part of the framework, a method is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. This method makes use of monogenic phase features into a cascade of pre-trained classifiers to eliminate unwanted regions. The second part of the framework is a part-based object detection model for searching specific targets which are considered as threat objects. The third part of the framework is to assess the severity of the threats to pipelines in terms of computing the geolocation and the temperature information of the threat objects. The proposed scheme is tested on the real-world dataset that were captured along the pipeline RoW.

Extreme learning machine with variance inflation factor for robust pattern recognition

Extreme learning machine (ELM), as a single hidden layer feedforward neural network, has shown very effective performance in pattern analysis and machine intelligence; however, there are some limitations that constrain the performance of ELM, such as data multicollinearity issues. The generalization capability of ELM could be significantly deteriorated when multicollinearity is present in the hidden layer output matrix which causes the matrix to become singular or ill-conditioning. To overcome such a problem, ridge regression can be utilized. The conventional way to avoid multicollinearity in ELM is achieved by precisely adjusting the ridge constant, which may not be a sophisticate solution to obtain the optimal value. In this paper, we present a solution for finding a satisfactory ridge constant by incorporating variance inflation factors (VIF) during calculating output weights in ELM, we termed this technique as ELM-VIF. Experimental results on handwritten digit recognition show that the proposed ELM-VIF, compared with the original ELM, has better stability and generalization performance.

Boosted ringlet features for robust object tracking

Accurate and efficient object tracking is an important aspect of various security and surveillance applications. In object tracking solutions which utilize intensity-based histogram feature methods for use on wide area motion imagery (WAMI), there currently exists tracking challenges due to object structural information distortions and pavement/background variations. The inclusion of structural target information including edge features in addition to the intensity features will allow for more robust object tracking. To achieve this we propose a feature extraction method that utilizes the Frei-Chen edge detector and Gaussian ringlet feature mapping. Frei-Chen edge detector extracts edge, line, and mean features that can be used to represent the structural features of the target. Gaussian ringlet feature mapping is used to obtain rotational invariant features that are robust to target and viewpoint rotation. These aspects are combined to create an efficient and robust tracking scheme. The proposed scheme is evaluated against state-of-the-art feature tracking methods using both temporal and spatial robustness metrics. The evaluations yield more accurate results for the proposed method on challenging WAMI sequences.

Scene sketch generation using mixture of gradient kernels and adaptive thresholding

This paper presents a simple but effective algorithm for scene sketch generation from input images. The proposed algorithm combines the edge magnitudes of directional Prewitt differential gradient kernels with Kirsch kernels at each pixel position, and then encodes them into an eight bit binary code which encompasses local edge and texture information. In this binary encoding step, relative variance is employed to determine the object shape in each local region. Using relative variance enables object sketch extraction totally adaptive to any shape structure. On the other hand, the proposed technique does not require any parameter to adjust output and it is robust to edge density and noise. Two standard databases are used to show the effectiveness of the proposed framework.

Tracking visual objects using pyramidal rotation invariant features

Challenges in object tracking such as object deformation, occlusion, and background variations require a robust tracker to ensure accurate object location estimation. To address these issues, we present a Pyramidal Rotation Invariant Features (PRIF) that integrates Gaussian Ringlet Intensity Distribution (GRID) and Fourier Magnitude of Histogram of Oriented Gradients (FMHOG) methods for tracking objects from videos in challenging environments. In this model, we initially partition a reference object region into increasingly fine rectangular grid regions to construct a pyramid. Histograms of local features are then extracted for each level of pyramid. This allows the appearance of a local patch to be captured at multiple levels of detail to make the algorithm insensitive to partial occlusion. Then GRID and magnitude of discrete Fourier transform of the oriented gradient are utilized to achieve a robust rotation invariant feature. The GRID feature creates a weighting scheme to emphasize the object center. In the tracking stage, a Kalman filter is employed to estimate the center of the object search regions in successive frames. Within the search regions, we use a sliding window technique to extract the PRIF of candidate objects, and then Earth Mover’s Distance (EMD) is used to classify the best matched candidate features with respect to the reference. Our PRIF object tracking algorithm is tested on two challenging Wide Area Motion Imagery (WAMI) datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness. Experimental results show that the proposed PRIF approach yields superior results compared to state-of-the-art feature based object trackers.