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--- projects [2013/04/19 20:05] – mb
+++ projects [2013/04/29 12:27] (current) – mb
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-====== Currently offered Projects, Winter 2013 (updated January 9, 2013)  ======
+====== Available Projects  ======
-(Listed in order received.)
+Currently offered Projects, Summer 2013
+(some projects still subject to confirmation)
+======Tracking and Activity Recognition Through Consensus in Distributed Camera Networks======
+**Supervisor**: Amir Asif
+**Required Background: ** Computer Vision or Signal and Systems Course preferred; Matlab; Interest in Signal/Image Processing
+**Short Description: **Over the past decade, large-scale camera networks have become increasingly popular in a wide range of applications, including: (i) Sports analysis; (ii) Security and surveillance; (iii) disaster response, and; (iv) Environmental modeling, where the objective is to follow the trajectory of a key target, for example, a star player in a soccer game or a suspect in a surveillance environment. In many applications, bandwidth constraints, security concerns, and difficulty in storing and analyzing large amounts of image data centrally at a single location necessitate the development of distributed camera network architectures. In this project, we investigate distributed scene analysis algorithms, where each camera estimates certain parameters of the target using a signal processing algorithm based upon its own set of observations. The local estimates are then shared with the neighboring cameras in an iterative, goosip-type fashion, and a final estimate is computed across the network using consensus algorithms. The selected student will develop Matlab code to apply distributed signal processing algorithms [1,2] that have been developed in the Signal Processing and Communications lab for target tracking and activity recognition in distributed camera networks.
+[1] A. Mohammadi and A. Asif, Distributed Particle Filter Implementation with Intermittent/Irregular Consensus Convergence, IEEE Transactions on Signal Processing, 2013. http://arxiv.org/abs/1112.2431.
+[2] A. Mohammadi and A. Asif, Decentralized Sensor Selection based on the Distributed Posterior Cramer-Rao Lower Bound, in proceedings of IEEE International Conference on Information Fusion, Singapore, 2012. pp. 1668-1675.
 ====== 3D Drawing System with Leap Motion finger tracker ======
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 The 3Gear system, threegear.com, lets users control a computer with their hands and fingers. This project creates a new 3D drawing system that enables users to quickly generate and modify 3D solids.
-====== Comparison of Finger Tracking systems ======
-**Supervisor**: Wolfgang Stuerzlinger
-**Required Background**: C/C++ coding
-This project compares the Leap Motion and the 3Gear System against each other in a comparative Fitts' law study
 ====== Tilt Target Selection on Touchscreen Phones ======
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   - Implement algorithms for detecting hand-raises based upon this investigation
   - Evaluate these algorithms in a real-classroom setting, using proprietary attentive sensing technology
+====== Attentive Sensing for Sport Video Recording Markets ======
+**Supervisor**: James Elder
+**Required Background**: Good programming skills; Good math skills; Knowledge of C and MATLAB programming languages
+The goal of this project is to modify York University’s patented attentive sensor technology to the sport video recording market.  Specific application domains under investigation include skiing, indoor BMX parks, and horse tracks.
+The general problem is to use attentive sensing technology (www.elderlab.yorku.ca) to visually detect and track multiple moving agents (e.g., skiers, riders, horses) and to select specific agents for active high-resolution smooth pursuit.
+The student will work with senior graduate students, postdoctoral fellows and research scientists to help modify the attentive sensing technology to operate in these domains.   Specific tasks include:
+.     Ground-truth available datasets
+.     Evaluate current attentive algorithms on these datasets
+.     Modify these algorithms to improve performance on these datasets
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-====== Numerical Methods ======
-**Supervisor**: Mike McNamee
-**Required Background**: Good grade in a Numerical Methods course and good knowledge
-of Fortran, C or similar language.
-**Description**: Write, debug and run several Fortran programs related to solving
-polynomial equations, with a view to comparing different known methods.
 ====== Enabling SaaS access to an experimental AI planner ======