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 scene(More)
Object detection and classification have received increased attention recently from computer vision and image processing researchers. Image processing views this problem at a much lower level as compared to machine learning and linear algebraic analysis which focus on the overall statistics of object classes given sufficient data. A good algorithm uses both(More)
Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply(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)
Object recognition algorithms often focus on determining the class of a detected object in a scene. Two significant phases are usually involved in object recognition. The first phase is the object representation phase, in which the most suitable features that provide the best discriminative power under constraints such as lighting, resolution, scale, and(More)
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(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)
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 arena(More)
The 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(More)