Combining Edge and Texture Planar Trackers based on a Local Quality Metric
A new probabilistic tracking framework for integrating information available from various visual cues is presented in this paper. The framework allows selection of ``good'' features for each cue, along with factors of their ``goodness'' to select the best combination form. Two particle filter based trackers, which use edge and texture features, run independently. The output of the master tracker is computed using democratic integration using the ``goodness'' weights. The final output is used as apriori for both tracker in the next iteration. Finally, particle filters are used to deal with non-Gaussian errors in feature extraction / prior computation. Results are shown for planar object tracking.
Frequency Domain Visual Servoing using Planar Contours
Fourier domain methods have had a long association with geometric vision. In this paper, we introduce Fourier domain methods into the field of visual servoing for the first time. We show how different properties of Fourier transforms may be used to address specific issues in traditional visual servoing methods, giving rise to algorithms that are more flexible. Specifically, we demonstrate how Fourier analysis may be used to address issues like decoupling of rotation and translation in 5 DOF, path following and correspondence-less visual servoing. Most importantly, by introducing Fourier techniques, we set a framework into which robust Fourier based geometry processing algorithms may be incorporated to address the various issues in servoing.
A Vision System for Monitoring Intermodal Freight Trains
We describe the design and implementation of a vision based Intermodal Train Monitoring System(ITMS) for extracting various features like length of gaps in an intermodal( IM) train which can later be used for higher level inferences. An intermodal train is a freight train consisting of two basic types of loads - containers and trailers. Our system first captures the video of an IM train, and applies image processing and machine learning techniques developed in this work to identify the various types of loads as containers and trailers. The whole process relies on a sequence of following tasks - robust background subtraction in each frame of the video, estimation of train velocity, creation of mosaic of the whole train from the video and classification of train loads into containers and trailers. Finally, the length of gaps between the loads of the IM train is estimated and is used to analyze the aerodynamic efficiency of the loading pattern of the train, which is a critical aspect of freight trains. This paper focusses on the machine vision aspect of the whole system.