6 research outputs found
Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram
The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host
millions of pilgrims every year. During Hajj, the movement of large number of
people has a unique spatial and temporal constraints, which makes Hajj one of
toughest challenges for crowd management. In this paper, we propose a computer
vision based framework that automatically analyses video sequence and computes
important measurements which include estimation of crowd density,
identification of dominant patterns, detection and localization of congestion.
In addition, we analyze helpful statistics of the crowd like speed, and
direction, that could provide support to crowd management personnel. The
framework presented in this paper indicate that new advances in computer vision
and machine learning can be leveraged effectively for challenging and high
density crowd management applications. However, significant customization of
existing approaches is required to apply them to the challenging crowd
management situations in Masjid Al Haram. Our results paint a promising picture
for deployment of computer vision technologies to assist in quantitative
measurement of crowd size, density and congestion.Comment: 17th Scientific Meeting on Hajj & Umrah Research, 201
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
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
Characterizing Human Behaviours Using Statistical Motion Descriptor
Identifying human behaviors is a challenging research problem due to the
complexity and variation of appearances and postures, the variation of camera
settings, and view angles. In this paper, we try to address the problem of
human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of
temporal segments. Then for each segment, we compute dense optical flow, which
provides instantaneous velocity information for all the pixels. We then compute
Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a
descriptor vector of 192- dimensions. We then train a non-linear multi-class
SVM that classify different human behaviors with the accuracy of 72.1%. We
evaluate our method by using publicly available human action data set.
Experimental results shows that our proposed method out performs state of the
art methods
Anomalous Situation Detection in Complex Scenes
In this paper we investigate a robust method to identify anomalies in complex
scenes. This task is performed by evaluating the collective behavior by
extracting the local binary patterns (LBP) and Laplacian of Gaussian (LoG)
features. We fuse both features together which are exploited to train an MLP
neural network during the training stage, and the anomaly is identified on the
test samples. Considering the challenge of tracking individuals in dense
crowded scenes due to multiple occlusions and clutter, in this paper we extract
LBP and LoG features and use them as an approximate representation of the
anomalous situation. These features well match the appearance of anomaly and
their consistency, and accuracy is higher both in regular and irregular areas
compared to other descriptors. In this paper, these features are exploited as
input prior to train the neural network. The MLP neural network is subsequently
explored to consider these features that can detect the anomalous situation.
The experimental tests are conducted on a set of benchmark video sequences
commonly used for anomaly situation detection