Ahmed Tashrif Kamal

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Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is(More)
Over the past decade, large-scale camera networks have become increasingly prevalent in a wide range of applications, such as security and surveillance, disaster response, and environmental modeling. In many applications, bandwidth constraints, security concerns, and difficulty in storing and analyzing large amounts of data centrally at a single location(More)
Due to their high fault-tolerance, ease of installation and scalability to large networks, distributed algorithms have recently gained immense popularity in the sensor networks community, especially in computer vision. Multi-target tracking in a camera network is one of the fundamental problems in this domain. Distributed estimation algorithms work by(More)
—Due to their high fault-tolerance and scalability to large networks, consensus-based distributed algorithms have recently gained immense popularity in the sensor networks community. Large scale camera networks are a special case. In a consensus-based state estimation framework, multiple neighboring nodes iteratively communicate with each other, exchanging(More)
Acknowledgments In the past few years of my academic endeavor, I had an amazing journey through the vast ocean of knowledge with some of the most amazing people of my time. I would like to start by expressing my deepest gratitude to my advisor Dr. Chong Ding. I did learn a lot by collaborating with them at the foundation stage of my PhD research. Due to all(More)
Human-activity recognition is one of the most challenging problems in computer vision. Researchers from around the world have tried to solve this problem and have come a long way in recognizing simple motions and atomic activities. As the computer vision community heads toward fully recognizing human activities, a challenging and labeled dataset is needed.(More)
— Distributed analysis of video captured by a large network of cameras has received significant attention lately. Tracking moving targets is one of the most fundamental tasks in this regard and the well-known Kalman Consensus Filter (KCF) has been applied to this problem. However, existing solutions do not consider the specific characteristics of video(More)
In this work, we consider a camera network where processing is distributed across the cameras. Our goal is to recognize actions of multiple targets consistently observed over the entire network. To obtain consistent and better results we need to properly fuse the action scores from multiple cameras. There have been multiple works on distributed tracking and(More)
Over the past decade, large-scale camera networks have become increasingly prevalent in a wide range of applications, like security and surveillance, disaster response and environmental modeling. In many applications, constraints of bandwidth, security concerns, and difficulty in storing and analyzing huge amounts data centrally at a single location(More)