1,995 research outputs found
Dynamic Matrix Decomposition for Action Recognition
Designing a technique for the automatic analysis of different actions in
videos in order to detect the presence of interested activities is of high
significance nowadays. In this paper, we explore a robust and dynamic
appearance technique for the purpose of identifying different action
activities. We also exploit a low-rank and structured sparse matrix
decomposition (LSMD) method to better model these activities.. Our method is
effective in encoding localized spatio-temporal features which enables the
analysis of local motion taking place in the video. Our proposed model use
adjacent frame differences as the input to the method thereby forcing it to
capture the changes occurring in the video. The performance of our model is
tested on a benchmark dataset in terms of detection accuracy. Results achieved
with our model showed the promising capability of our model in detecting action
activities
Crowd Management in Open Spaces
Crowd analysis and management is a challenging problem to ensure public
safety and security. For this purpose, many techniques have been proposed to
cope with various problems. However, the generalization capabilities of these
techniques is limited due to ignoring the fact that the density of crowd
changes from low to extreme high depending on the scene under observation. We
propose robust feature based approach to deal with the problem of crowd
management for people safety and security. We have evaluated our method using a
benchmark dataset and have presented details analysis
Crowded Scene Analysis: A Survey
Automated scene analysis has been a topic of great interest in computer
vision and cognitive science. Recently, with the growth of crowd phenomena in
the real world, crowded scene analysis has attracted much attention. However,
the visual occlusions and ambiguities in crowded scenes, as well as the complex
behaviors and scene semantics, make the analysis a challenging task. In the
past few years, an increasing number of works on crowded scene analysis have
been reported, covering different aspects including crowd motion pattern
learning, crowd behavior and activity analysis, and anomaly detection in
crowds. This paper surveys the state-of-the-art techniques on this topic. We
first provide the background knowledge and the available features related to
crowded scenes. Then, existing models, popular algorithms, evaluation
protocols, as well as system performance are provided corresponding to
different aspects of crowded scene analysis. We also outline the available
datasets for performance evaluation. Finally, some research problems and
promising future directions are presented with discussions.Comment: 20 pages in IEEE Transactions on Circuits and Systems for Video
Technology, 201
Deep Trajectory for Recognition of Human Behaviours
Identifying human actions in complex scenes is widely considered as a
challenging research problem due to the unpredictable behaviors and variation
of appearances and postures. For extracting variations in motion and postures,
trajectories provide meaningful way. However, simple trajectories are normally
represented by vector of spatial coordinates. In order to identify human
actions, we must exploit structural relationship between different
trajectories. In this paper, we propose a method that divides the video into N
number of segments and then for each segment we extract trajectories. We then
compute trajectory descriptor for each segment which capture the structural
relationship among different trajectories in the video segment. For trajectory
descriptor, we project all extracted trajectories on the canvas. This will
result in texture image which can store the relative motion and structural
relationship among the trajectories. We then train Convolution Neural Network
(CNN) to capture and learn the representation from dense trajectories. .
Experimental results shows that our proposed method out performs state of the
art methods by 90.01% on benchmark data set
AED-Net: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a long
time. In this paper, a self-supervised framework, abnormal event detection
network (AED-Net), which is composed of PCAnet and kernel principal component
analysis (kPCA), is proposed to address this problem. Using surveillance video
sequences of different scenes as raw data, PCAnet is trained to extract
high-level semantics of crowd's situation. Next, kPCA,a one-class classifier,
is trained to determine anomaly of the scene. In contrast to some prevailing
deep learning methods,the framework is completely self-supervised because it
utilizes only video sequences in a normal situation. Experiments of global and
local abnormal event detection are carried out on UMN and UCSD datasets, and
competitive results with higher EER and AUC compared to other state-of-the-art
methods are observed. Furthermore, by adding local response normalization (LRN)
layer, we propose an improvement to original AED-Net. And it is proved to
perform better by promoting the framework's generalization capacity according
to the experiments.Comment: 14 pages, 7 figure
Salient Object Detection: A Distinctive Feature Integration Model
We propose a novel method for salient object detection in different images.
Our method integrates spatial features for efficient and robust representation
to capture meaningful information about the salient objects. We then train a
conditional random field (CRF) using the integrated features. The trained CRF
model is then used to detect salient objects during the online testing stage.
We perform experiments on two standard datasets and compare the performance of
our method with different reference methods. Our experiments show that our
method outperforms the compared methods in terms of precision, recall, and
F-Measure
A deep learning approach for analyzing the composition of chemometric data
We propose novel deep learning based chemometric data analysis technique. We
trained L2 regularized sparse autoencoder end-to-end for reducing the size of
the feature vector to handle the classic problem of the curse of dimensionality
in chemometric data analysis. We introduce a novel technique of automatic
selection of nodes inside the hidden layer of an autoencoder through Pareto
optimization. Moreover, Gaussian process regressor is applied on the reduced
size feature vector for the regression. We evaluated our technique on orange
juice and wine dataset and results are compared against 3 state-of-the-art
methods. Quantitative results are shown on Normalized Mean Square Error (NMSE)
and the results show considerable improvement in the state-of-the-art.Comment: 6 pages, 1 figure, 1 tabl
AAD: Adaptive Anomaly Detection through traffic surveillance videos
Anomaly detection through video analysis is of great importance to detect any
anomalous vehicle/human behavior at a traffic intersection. While most existing
works use neural networks and conventional machine learning methods based on
provided dataset, we will use object recognition (Faster R-CNN) to identify
objects labels and their corresponding location in the video scene as the first
step to implement anomaly detection. Then, the optical flow will be utilized to
identify adaptive traffic flows in each region of the frame. Basically, we
propose an alternative method for unusual activity detection using an adaptive
anomaly detection framework. Compared to the baseline method described in the
reference paper, our method is more efficient and yields the comparable
accuracy
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning
In crowded scenes, detection and localization of abnormal behaviors is
challenging in that high-density people make object segmentation and tracking
extremely difficult. We associate the optical flows of multiple frames to
capture short-term trajectories and introduce the histogram-based shape
descriptor referred to as shape contexts to describe such short-term
trajectories. Furthermore, we propose a K-NN similarity-based statistical model
to detect anomalies over time and space, which is an unsupervised one-class
learning algorithm requiring no clustering nor any prior assumption. Firstly,
we retrieve the K-NN samples from the training set in regard to the testing
sample, and then use the similarities between every pair of the K-NN samples to
construct a Gaussian model. Finally, the probabilities of the similarities from
the testing sample to the K-NN samples under the Gaussian model are calculated
in the form of a joint probability. Abnormal events can be detected by judging
whether the joint probability is below predefined thresholds in terms of time
and space, separately. Such a scheme can adapt to the whole scene, since the
probability computed as such is not affected by motion distortions arising from
perspective distortion. We conduct experiments on real-world surveillance
videos, and the results demonstrate that the proposed method can reliably
detect and locate the abnormal events in the video sequences, outperforming the
state-of-the-art approaches
Energy-based Models for Video Anomaly Detection
Automated detection of abnormalities in data has been studied in research
area in recent years because of its diverse applications in practice including
video surveillance, industrial damage detection and network intrusion
detection. However, building an effective anomaly detection system is a
non-trivial task since it requires to tackle challenging issues of the shortage
of annotated data, inability of defining anomaly objects explicitly and the
expensive cost of feature engineering procedure. Unlike existing appoaches
which only partially solve these problems, we develop a unique framework to
cope the problems above simultaneously. Instead of hanlding with ambiguous
definition of anomaly objects, we propose to work with regular patterns whose
unlabeled data is abundant and usually easy to collect in practice. This allows
our system to be trained completely in an unsupervised procedure and liberate
us from the need for costly data annotation. By learning generative model that
capture the normality distribution in data, we can isolate abnormal data points
that result in low normality scores (high abnormality scores). Moreover, by
leverage on the power of generative networks, i.e. energy-based models, we are
also able to learn the feature representation automatically rather than
replying on hand-crafted features that have been dominating anomaly detection
research over many decades. We demonstrate our proposal on the specific
application of video anomaly detection and the experimental results indicate
that our method performs better than baselines and are comparable with
state-of-the-art methods in many benchmark video anomaly detection datasets
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