Object Tracking and Mensuration in Surveillance Videos

This thesis focuses on tracking and mensuration in surveillance videos. The first part of the thesis discusses several object tracking approaches based on the different properties of tracking targets. For airborne videos, where the targets are usually small and with low resolutions, an approach of building motion models for foreground/background proposed in which the foreground target is simplified as a rigid object. For relatively high resolution targets, the non-rigid models are applied. An active contour-based algorithm has been introduced. The algorithm is based on decomposing the tracking into three parts: estimate the affine transform parameters between successive frames using particle filters; detect the contour deformation using a probabilistic deformation map, and regulate the deformation by projecting the updated model onto a trained shape subspace. The active appearance Markov chain (AAMC). It integrates a statistical model of shape, appearance and motion. In the AAMC model, a Markov chain represents the switching of motion phases (poses), and several pairwise active appearance model (P-AAM) components characterize the shape, appearance and motion information for different motion phases. The second part of the thesis covers video mensuration, in which we have proposed a heightmeasuring algorithm with less human supervision, more flexibility and improved robustness. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene. We then apply a single view mensuration algorithm to each of the frames to obtain height measurements. Finally, using the LMedS as the cost function and the Robbins-Monro stochastic approximation (RMSA) technique to obtain the optimal estimate

Data Collection and Machine Learning Methods for Automated Pedestrian Facility Detection and Mensuration

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view imagery. We test data from these two viewpoints individually and with an ensemble method that we refer to as our “dual-perspective prediction model”. In order to obtain this data, we developed a data collection pipeline that combines crowdsourced pedestrian facility location data with aerial and street-view imagery from Bing Maps. In addition to the Convolutional Neural Network used to perform pedestrian facility detection using this data, we also trained a segmentation network to measure the length and width of crosswalks from aerial images. In our tests with a dual-perspective image dataset that was heavily occluded in the aerial view but relatively clear in the street view, our dual-perspective prediction model was able to increase prediction accuracy, recall, and precision by 49%, 383%, and 15%, respectively (compared to using a single perspective model based on only aerial view images). In our tests with satellite imagery provided by the Mississippi Department of Transportation, we were able to achieve accuracies as high as 99.23%, 91.26%, and 93.7% for aerial crosswalk detection, aerial sidewalk detection, and aerial crosswalk mensuration, respectively. The final system that we developed packages all of our machine learning models into an easy-to-use system that enables users to process large batches of imagery or examine individual images in a directory using a graphical interface. Our data collection and filtering guidelines can also be used to guide future research in this area by establishing standards for data quality and labelling

Evaluation of Truck Dispatch System and its Application using GPS in Opencast Mines- a Case Study of Indian Mines

Truck haulage now a days is the most common means which is used for moving ore/waste in open-cast mining operations. The truck haulage is usually the costliest unit operation in a truck shovel open cast mining. The advancement in computer coding technology has advanced to a point where there are many truck dispatching systems which will give the potential of advancing truck-shovel productivity and future savings. By trying a dispatching system in any mine can give operational increase in production by minimizing waiting times and can give other beneficial advantages and can also be obtained through good monitoring, optimal routing. The capacity of the employed truck-shovel fleet counts on the dispatching methodology in use, the intricacy of the truck shovel system and a number of other variables. It is a very common situation in mining that considerable number of analysis of the available techniques is undertaken before dispatching is done. In many number of cases, computer simulation is the better applicable and effective method of relating the alternative dispatching strategies. Keeping this in mind computer programs are developed using C++ language for the monitoring of the equipment performance in truck dispatch system in opencast mines. To study about the truck dispatch system (TDS), we have made a choice to make it on the shovel dumper combination using GPS. In TDS system the computer monitors the location and status whether the dumper is full or empty and its heading, velocity of each vehicle in the fleet. The system analyses production numbers, such as haul routes, historic data about drive time to a specific shovel location and the cycle time and time taken to make a complete trip, trip from the shovel to the dump site and back

Centers for the commercial development of space

In 1985, NASA initiated an innovative effort called Centers for the Commercial Development of Space (CCDS). The CCDS program was designed to increase private-sector interest and investment in space-related activities, while encouraging U.S. economic leadership and stimulating advances in promising areas of research and development. Research conducted in the Centers handling the following areas is summarized: materials processing; life sciences; remote sensing; automation and robotics; space propulsion; space structures and materials; and space power

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

Topography of aortic heart valves

The cooperative effort towards the development of a tri-leaflet prosthetic heart valve is described. The photogrammetric studies were conducted on silicone rubber molds. Information on data acquisition and data reduction phases is given, and certain accuracy aspects of the project are explained. The various outputs which are discussed include digital models, profiles, and contour maps

Joint acoustic-video fingerprinting of vehicles, part II

In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data

The Nature of Notebooks: How Enlightenment Schoolchildren Transformed the Tabula Rasa

John Locke's comparison of the mind to a blank piece of paper, the tabula rasa, was one of the most recognizable metaphors of the British Enlightenment. Though scholars embrace its impact on the arts, humanities, natural sciences, and social sciences, they seldom consider why the metaphor was so successful. Concentrating on the notebooks made and used by the schoolchildren of Enlightenment Scotland, this essay contends that the answer lies in the material and visual conditions that gave rise to the metaphor's usage. By the time students had finished school, they had learned to conceptualize the pages, the script, and the figures of their notebooks as indispensable learning tools that could be manipulated by scores of adaptable folding, writing, and drawing techniques. In this article, I reveal that historicizing the epistemology and manipulability of student manuscript culture makes it possible to see that the success of Locke's metaphor was founded on its appeal to everyday note-keeping activities performed by British schoolchildren