Browsing by Author "Somasundaram, Guruprasad"
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Item Deployment of Practical Methods for Counting Bicycle and Pedestrian Use of a Transportation Facility(Intelligent Transportation Systems Institute, Center for Transportation Studies, 2012-01) Somasundaram, Guruprasad; Morellas, Vassilios; Papanikolopoulos, NikolaosThe classification problem of distinguishing bicycles from pedestrians for traffic counting applications is the objective of this research project. The scenes that are typically involved are bicycle trails, bridges, and bicycle lanes. These locations have heavy traffic of mainly pedestrians and bicyclists. A vision-based system overcomes many of the shortcomings of existing technologies such as loop counters, buried pressure pads, infra-red counters, etc. These methods do not have distinctive profiles for bicycles and pedestrians. Also most of these technologies require expert installation and maintenance. Cameras are inexpensive and abundant and are relatively easy to use, but they tend to be useful as a counting system only when accompanied by powerful algorithms that analyze the images. We employ state-of-the-art algorithms for performing object classification to solve the problem of distinguishing bicyclists from pedestrians. We detail the challenges that are involved in this particular problem, and we propose solutions to address these challenges. We explore common approaches of global image analysis aided by motion information and compare the results with local image analysis in which we attempt to distinguish the individual parts of the composite object. We compare the classification accuracies of both approaches on real data and present detailed discussion on practical deployment factors.Item Global self-similarity and saliency measures based on sparse representations for classification of objects and spatio-temporal sequences(2012-12) Somasundaram, GuruprasadExtracting the truly salient regions in images is critical for many computer vision applications. Salient regions are considered the most informative regions of an image. Traditionally these salient regions have always been considered as local phenomena in which the salient regions stand out as local extrema with respect to their immediate neighbors. We introduce a novel global saliency metric based on sparse representation in which the regions that are most dissimilar with respect to the entire image are deemed salient. We examine our definition of saliency from the theoretical stand point of sparse representation and minimum description length. Encouraged by the efficacy of our method in modeling foreground objects, we propose two classification methods for recognizing objects in images. First, we introduce two novel global self-similarity descriptors for object representation which can directly be used in any classification framework. Next, we use our salient feature detection approach with conventional region descriptors in a bag-of-features framework. Experimentally we show that our feature detection method enhances the bag-of-features framework. Finally, we extend our salient bag-of-features approach to the spatio-temporal domain for use with three-dimensional dense descriptors. We apply this method successfully to video sequences involving human actions. We obtain state-of-the-art recognition rates in three distinct datasets involving sports and movie actions.Item Monitoring the Use of HOV and HOT Lanes(Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-01) Holec, Eric; Somasundaram, Guruprasad; Papanikolopoulos, Nikolaos; Morellas, VassiliosThis report presents the formulation and implementation of an automated computer vision and machine learning based system for estimation of the occupancy of passenger vehicles in high-occupancy vehicles and highoccupancy toll (HOV/HOT) lanes. We employ a multi-modal approach involving near-infrared images and highresolution color video images in conjunction with strong maximum margin based classifiers such as support vector machines. We attempt to maximize the information that can be extracted from these two types of images by computing different features. Then, we build classifiers for each type of feature which are compared to determine the best feature for each imaging method. Based on the performance of the classifiers we critique the efficacy of the individual approaches as the costs involved are significantly different.Item Practical Methods for Analyzing Pedestrian and Bicycle Use of a Transportation Facility(Minnesota Department of Transportation Office of Research Services, 2010-02) Somasundaram, Guruprasad; Morellas, Vassilios; Papanikolopoulos, Nikolaos P.The objective of the project is to analyze existing technologies used for the process of generating counts of bicycles and pedestrians in transportation facilities such as walk and bicycle bridges, urban bicycle routes, bicycle trails etc. The advantages and disadvantages of each existing technology which is being applied to counting has been analyzed and some commercially available products were listed. A technical description of different methods that were considered for vision based object recognition is also mentioned along with the reasons as to why such methods were overlooked for our problem. Support Vector Machines were used for classification based on a vocabulary of features built using interest point detectors. After finalizing the software and hardware, five sites were picked for filming and about 10 hours of video was acquired in all. A portion of the video data was used for training and the remainder was used for testing the algorithm’s accuracy. Results of counts are provided and an interpretation of these results is provided in this report. Upon detailed analysis the reasons for false counts and undercounting in some cases have been identified and current work concerns dealing with these issues. Changes are being made to the system to improve the accuracy with the current level of training and make the system available for practitioners to perform counting.Item Warning Efficacy of Active Versus Passive Warnings for Unsignalized Intersection and Mid-Block Pedestrian Crosswalks(Minnesota Department of Transportation, 2009-01) Smith, Thomas J.; Hammond, Curtis; Somasundaram, Guruprasad; Papanikolopoulos, NikolaosThis study evaluated the efficacy of active versus passive warnings at uncontrolled pedestrian (ped) crosswalks (Xwalks), by comparing how these two warnings types influenced behavior of drivers approaching such Xwalks. Vehicle-Xwalk interactions were observed at 18 sites with passive, continuously flashing, or ped-activated warnings, yielding 7,305 no ped present and 596 ped present interactions. Vehicle velocities and accelerations were averaged for each interaction. Findings show no significant effect of warning type on overall velocities for either interaction type. With peds present only, for average velocities at successive 5m distances from the Xwalk, a downward trend in velocities from 25 to 5m is observed for passive and active warning sites, but not for pedactivated warning sites. Various lines of evidence point to a number of sources of ambiguity regarding the salience of uncontrolled Xwalk warnings, resulting in behavioral uncertainty by drivers interacting with such warnings. Mixed findings on effects of warning type in this study point to the need for further analysis of this problem area.