Oct 2013. PNNL Parking Lot 2 dataset as well as the tracking groundtruth are released.
Oct 2013. Code for CVPR13 paper is released.
Oct 2013. GMCP tracking code is being updated.
ALADDIN:Automated Low-Level Analysis and Description of Diverse Intelligence Video
The Automated Low-Level Analysis and Description of Diverse Intelligence Video (ALADDIN)
Program seeks to combine the state-of-the-art in video extraction, audio extraction, knowledge representation,
and search technologies in a revolutionary way to create a fast, accurate, robust,
and extensible technology that supports the multimedia analytic needs of the future.
UCF is a part of SRI-Sarnoff team and I've been leading UCF team
since summer 2012. I have investigated the benefits of using
concepts,attribute and objects in representating a video. I am also interested in
problem of event detection using only few examplars.[More Info]
Evaluation of Tracking Algorithms on ISIS Video Data for the Wide Area Surveillance Project:
The project is a part of the Wide-Area Surveillance (WAS) project
being implemented by the U.S. Department of Homeland Security (DHS)
Science and Technology Directorate (S&T).
This project targets the development and evaluation of desirable
crowd tracking algorithm to be used in the (ISIS) context.
ISIS is a camera system developed by Massachusetts Institute of
Technology/Lincoln Laboratories (MIT/LL) and managed by Pacific
Northwest National Laboratory (PNNL). The ISIS consists of a 100 Mpixel sensor (an array of image servers and associated hard drive storage array).
While the ISIS camera can collect a large volume of video data for a wide area monitored,
it demands an effective crowd tracking algorithm to be integrated
with the ISIS software system supporting video viewing and analysis.[More Info]
The first method that we proposed was a part-based greedy approach
capable of detecting occluded part of a person and was published in CVPR2012.The
second approach was based on a global data association method which we
utilized and introduced Generalized Minimum Clique Graph to efficiently track
each individual in video sequences provided. The later was published in ECCV2012.
Visual analytics in multiple camera networks:
The project is a part of the Visual analytics project
being implemented by the the U.S. Army Research Laboratory, the U.S.
Army Research Office (ARO). The prject targets include: 1) Detection and tracking of humans in multiple,
disjoint camera videos, in potentially crowded scenarios, and learning of an adaptive appearance
model for individual identification and subsequent target reacquisition.
2) Inference of object movement patterns, location of move-stop-move conditions and entry and exit locations
in unobserved regions between pairs of cameras with disjoint fields of view. 3) Learning of scene semantics including
spatial and temporal relationships between diverse types of entities observed in multiple cameras,
where examples of such entities include, camera fields of view, entry and exit regions, object types,
and dominant paths.
We proposed a new graph theoritic approach (GMMCP) to solve the Multiple Object Tracking problem. We show great performance in several
benchmark. The work is published in CVPR 2015.
Who's Your Daddy?
In this project, our goal is to bridge computer vision research with findings in
anthropological studies to answer several key questions:
-Do offspring resemble their parents?
-Do offspring resemble one parent more than the other?
-What parts of the face are more genetic?
-Do anthropologies' studies help learn better features?
To answer these questions and address the problem of parent-offspring
resemblance we propose an algorithm that fuses the features and metrics discovered via gated
autoencoders with a discriminative neural network layer that learns the optimal, or what we
call genetic, features for the task. For more information please check out the CVPR14 paper.[Project Page][Press Release] [Fox35 Interview]
Target Identity-aware Network Flow for Online Multiple Target Tracking Afshin Dehghan, Yicong Tian, Philip. H. S. Torr and Mubarak Shah in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2015)
GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking Afshin Dehghan, Shayan Modiri Assari and Mubarak Shah in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2015)
Understanding Crowd Collectivity: A Meta-Tracking Approach Afshin Dehghan and Mahdi M. Kalayeh in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition Workshop(CVPRW 2015) SUNw: Scene Understanding Workshop
Improving Semantic Concept Detection through the Dictionary of Visually-distinct Elements Afshin Dehghan, Haroon Idrees and Mubarak Shah in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2014)
[Send Email for Dataset]
Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders Afshin Dehghan, Enrique G. Ortiz, Ruben Vilegas and Mubarak Shah in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2014)
[Project Page and Source Code]
Complex Event Recognition by Latent Temporal Models of Concepts
Ehsan Zare Borzeshi,Afshin Dehghan, Massimo Piccardi, and Mubarak Shah in Proceedings of IEEE International Conference on Image Processing (ICIP 2014)
Visual Business Recognition - A Multimodal Approach
Amir Roshan Zamir, Afshin Dehghan and Mubarak Shah In Proceeding of ACM International Conference on Multimedia (ACM MM'2013)
Improving an Object Detector and Extracting Regions using Superpixels
Guang Shu, Afshin Dehghan and Mubarak Shah In proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'13)
GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs
Amir Roshan Zamir, Afshin Dehghan and Mubarak Shah In proceedings of European Conference on Computer Vision 2012 (ECCV'12)
Keynote: Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities Afshin Dehghan, Haroon Idrees, Amir Roshan Zamir and Mubarak Shah In Proceedings of PED, June 2012
Part-based Multiple-Person Tracking with Partial Occlusion Handling
Guang Shu, Afshin Dehghan, Omar Oreifej, Emily Hand, Mubarak Shah In proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12)
SRI-Sarnoff AURORA System at TRECVID 2012 Multimedia Event Detection and Recounting
H. Cheng, J. Liu, S. Ali, O. Javed, Q. Yu,A. Tamrakar, A. Divakaran†, H. S. Sawhney, R. Manmatha, Ja. Allan, A. Hauptmann, M. Shah, S. Bhattacharya, A. Dehghan , G. Friedland, B. M. Elizalde, T. Darrell, M. Witbrock, J. Curtis,
In Proceeding of Trecvid Video Retrieval Evaluation Workshop, NIST, Gaitherburg, Md, November 2012