I am a Ph.D student at the University of Central Florida's Computer Vision Lab where I work with Dr. Mubarak Shah. My research interests lie primarily in the area of computer vision, more specifically, I am interested in object recognition, scene interpretation, human action recognition and machine learning.

To learn more about my research, please follow these links:


New: "Tracking in Unstructured Crowded Scenes" Accepted for ICCV 2009.

 

Project page coming soon.


Action MACH: Maximum Average Correlation Height filter for Action Classification

Mikel D. Rodriguez Sullivan, Javed Ahmed, Mubarak Shah.

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Alaska, 2008.

Visit the Action MACH project page 

A common limitation of template based methods is their inability to generate a single template using a collection of examples. Action MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotempoal volume), and vector valued data such as optical flow. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive et of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television.

Detecting and Segmenting Humans in Crowded Scenes

Mikel D. Rodriguez Sullivan, Mubarak Shah

ACM Multimedia Conference (ACMMM) 2007, Augsburg Germany. 

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This work represents an approach to detecting and segmenting humans with extensive posture articulations in crowded video sequences. In our method we learn a set of posture clusters, and a codebook of local shape distributions for humans in various postures. Instances of the codebook entries cast votes for locations of humans in the video and their respective postures. Subsequently, consistent hypotheses are found as maxima within a voting space. Finally, the segmentation of humans in the scene is initialized by the corresponding posture clusters and contours are evolved to obtain precise and consistent segmentations. Our experimental results indicate that the framework provides a simple yet effective means for aggregating shape-based cues. The proposed method is capable of detecting and segmenting humans in crowded scenes as they perform a diverse set of activities and undergo a wide range of articulations within different contexts.

Matlab source code

Visual surveillance in maritime port facilities

Mikel D. Rodriguez Sullivan, Mubarak Shah

SPIE Defense and Security Symposium 2008, Orlando, USA

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In this work we propose a method for securing port facilities which uses a set of video cameras to automatically detect various vessel classes moving within buffer zones and off-limit areas. Vessels are detected by an edge-enhanced spatio-temporal optimal trade-off maximum average correlation height filter which is capable of discriminating between vessel classes while allowing for intra-class variability. Vessel detections are cross-referenced with e-NOAD data in order to verify the vessel’s access to the port. Our approach does not require foreground/background modeling in order to detect
vessels, and therefore it is effective in the presence of the class of dynamic backgrounds, such as moving water, which are prevalent in port facilities. Furthermore, our approach is computationally efficient, thus rendering it more suitable for real-time port surveillance systems. We evaluate our method on a dataset collected from various port locations which contains a wide range of vessel classes.

Automated Inspection of Railroads 

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 The purpose of this research project is to develop an automated visual inspection/detection system for reliable identification and localization of structural defects in railroad tracks for the Florida Department of Transportation.

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Source Code

Datasets:

The New Infrared Dataset (NIRDS): Pedestrians and vehicles

UCF Sports Action Dataset

UCF Feature Films Action Dataset

Copyrights and patents