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Afshin Dehghan
PhD student (since Fall 2011)
Email: adehghan8@cs.ucf.edu (Remove 8) |
Publications
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GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs Abstract:
Data association is an essential component of any human tracking
system. The majority of current methods, such as bipartite matching, incorporate
a limited-temporal-locality of the sequence into the data association problem,
which makes them inherently prone to ID-switches and difficulties caused
by long-term occlusion, cluttered background, and crowded scenes. We purpose
a global approach to data association which incorporates both motion and appearance
in a global manner. Unlike limited-temporal-locality methods which
incorporate 2 or few frames into the data association problem, we incorporate the
whole temporal span and solve the data association problem for one object at a
time, while implicitly incorporating the rest of the objects. In order to achieve
this, we utilize Generalized Minimum Clique Graphs to solve the optimization
problem of our proposed data association method. Our proposed method yields
a better formulated approach to data association supported by our superior results.
Experiments show the proposed method makes significant improvements in
tracking in the diverse sequences of Town Center, TUD-crossing, TUDStadtmitte
, PETS2009, and a new sequence called Parking Lot compared
to the state of the art methods. |
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Keynote: Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities Abstract:
Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos.
Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos.
In this keynote paper, we present two state of the art methods for automatic pedestrian tracking in videos
with low and high crowd density. For videos with low density, first we detect each person using a part-based
human detector. Then, we employ a global data association method based on Generalized Graphs for tracking each individual
in the whole video. In videos with high crowd-density, we track in-dividuals using a scene structured force model and crowd flow modeling.
Addi-tionally, we present an alternative approach which utilizes contextual infor-mation without the need to learn the structure of the scene.
Performed evalua-tions show the presented methods outperform the currently available algorithms on several benchmarks. |
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Part-based Multiple-Person Tracking with Partial Occlusion Handling Abstract:
Single camera-based multiple person tracking is a very challenging
problem. The performance of state-of-the-art methods
is often hindered by difficulties such as occlusion and changes in
appearance which frequently occurin surveillance videos.
In this paper, we address such problems by proposing a robust
part-based tracking-bydetection framework. Human detection using part
models
has become quite popular, yet its extension in tracking has not
been fully explored. Our approach learns partbased
person-specific SVM classifiers which capture the articulations of
the human bodies in dynamically changing
appearance and background. The confidence from the person
classifier is employed along with motion and size cues to construct an
affinity matrix which is used to associate
the observations with the tracking through a greedy bipartite
approach. Our method handles partial occlusions
in both the detection and the tracking stages. In the detection
stage, we obtain a probabilistic representation
of the parts and accordingly select the subset of parts which
maximizes the probability of detection, which significantly
improves the detection performance in crowded scenes. In the
tracking stage, we dynamically handle occlusions
by distributing the score of the learned person classifier among
its corresponding parts, which allows us to detect
and predict partial occlusions, and prevent the performance of the
classifiers from being degraded. Extensive experiments
using the proposed method on several challenging sequences
demonstrate state-of-the-art performance in multiple-people tracking. |
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A Multi-Agent Architecture for Tracking User Interactions in Browser-based Games Abstract:
This paper presents a tracking system developed for browser-based games to track user interactions remotely.
To eliminate the restriction of accessing local file system, the client side of the system runs on a
standalone web server. The tracking agent writes the user interactions with a game on the local file system,
and the log-transfer agent sends the results to the server side of the system as soon as an internet connection
is available. This system is used in a research to investigate the effects of playing certain games on visual
working memory of children under the age 7. This tracking system has provided the games with better
user-friendliness by removing browser's permission prompts in JavaScript and Flash-based games.
Furthermore, allowing remote data collection from all the gamers provides vast data for future research. |