Guruprasad Somasundaram

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With the proliferation of security cameras, the approach taken to monitoring and placement of these cameras is critical. This paper presents original work in the area of multiple camera human activity monitoring. First, a system is presented that tracks pedestrians across a scene of interest and recognizes a set of human activities. Next, a framework is(More)
— The use of cameras is becoming more prevalent by the day owing to the variety of applications they have. However, each application requires a specific placement of the cameras for best performance. Therefore, determining this placement has been a problem of much work in the field of computer vision. However, most of the current approaches deal with a(More)
— Computer vision as an entire field has a wide and diverse range of applications. The specific application for this project was in the realm of dance, notably ballet and choreography. This project was proof-of-concept for a choreography assistance tool used to recognize and record dance movements demonstrated by a choreographer. Keeping the commercial(More)
The objective of object recognition algorithms in computer vision is to quantify the presence or absence of a certain class of objects, for e.g.: bicycles, cars, people, etc. which is highly useful in traffic estimation applications. Sparse signal models and dictionary learning techniques can be utilized to not only classify images as belonging to one class(More)
Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. One way to(More)
This 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 high-occupancy toll (HOV/HOT) lanes. We employ a multi-modal approach involving near-infrared images and high-resolution color video images in(More)
— Object classification is a widely researched area in the field of computer vision. Lately there has been a lot of attention to appearance based models for representing objects. The most important feature of classifying objects such as pedestrians, vehicles, etc. in traffic scenes is that we have motion information available to us. The motion information(More)