459 research outputs found
Visual Human Tracking and Group Activity Analysis: A Video Mining System for Retail Marketing
Thesis (PhD) - Indiana University, Computer Sciences, 2007In this thesis we present a system for automatic human tracking and activity recognition from
video sequences. The problem of automated analysis of visual information in order to derive descriptors
of high level human activities has intrigued computer vision community for decades and is
considered to be largely unsolved. A part of this interest is derived from the vast range of applications
in which such a solution may be useful. We attempt to find efficient formulations of these tasks
as applied to the extracting customer behavior information in a retail marketing context. Based on
these formulations, we present a system that visually tracks customers in a retail store and performs
a number of activity analysis tasks based on the output from the tracker.
In tracking we introduce new techniques for pedestrian detection, initialization of the body
model and a formulation of the temporal tracking as a global trans-dimensional optimization problem.
Initial human detection is addressed by a novel method for head detection, which incorporates
the knowledge of the camera projection model.The initialization of the human body model is addressed
by newly developed shape and appearance descriptors. Temporal tracking of customer
trajectories is performed by employing a human body tracking system designed as a Bayesian
jump-diffusion filter. This approach demonstrates the ability to overcome model dimensionality
ambiguities as people are leaving and entering the scene.
Following the tracking, we developed a two-stage group activity formulation based upon the
ideas from swarming research. For modeling purposes, all moving actors in the scene are viewed here as simplistic agents in the swarm. This allows to effectively define a set of inter-agent interactions,
which combine to derive a distance metric used in further swarm clustering. This way, in the
first stage the shoppers that belong to the same group are identified by deterministically clustering
bodies to detect short term events and in the second stage events are post-processed to form clusters
of group activities with fuzzy memberships.
Quantitative analysis of the tracking subsystem shows an improvement over the state of the
art methods, if used under similar conditions. Finally, based on the output from the tracker, the
activity recognition procedure achieves over 80% correct shopper group detection, as validated by
the human generated ground truth results
Change detection in combination with spatial models and its effectiveness on underwater scenarios
This thesis proposes a novel change detection approach for underwater scenarios and combines it with different especially developed spatial models, this allows accurate and spatially coherent detection of any moving objects with a static camera in arbitrary environments. To deal with the special problems of underwater imaging pre-segmentations based on the optical flow and other special adaptions were added to the change detection algorithm so that it can better handle typical underwater scenarios like a scene crowded by a whole fish swarm
Intelligent evacuation management systems: A review
Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios
ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS
Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on a histogram of oriented gradients and Markov random field easily captures varying dynamic of the crowded environment.Histogram of oriented gradients along with well-known Markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost.To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.
Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information
Applying people detectors to unseen data is challenging since patterns distributions, such
as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ
from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt
frame by frame people detectors during runtime classification, without requiring any additional
manually labeled ground truth apart from the offline training of the detection model. Such adaptation
make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors
estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation
discriminates between relevant instants in a video sequence, i.e., identifies the representative frames
for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration
(i.e., detection threshold) of each detector under analysis, maximizing the mutual information to
obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not
require training the detectors for each new scenario and uses standard people detector outputs, i.e.,
bounding boxes. The experimental results demonstrate that the proposed approach outperforms
state-of-the-art detectors whose optimal threshold configurations are previously determined and
fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R
(HAVideo
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