TITLE:
Multi-Dimension Support Vector Machine Based Crowd Detection and Localisation Framework for Varying Video Sequences
AUTHORS:
Manoharan Mahalakshmi, Radhakrishnan Kanthavel, Divakaran Thilagavathy Dinesh
KEYWORDS:
Multiple Support Vector Machine, Crowd Detection, Motion Blur, Collaborative Model, Gaber Feature
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.11,
September
8,
2016
ABSTRACT: In this
paper, we propose a novel method for anomalous crowd behaviour detection and
localization with divergent centers in intelligent video sequence through
multiple SVM (support vector machines) based appearance model. In
multi-dimension SVM crowd detection, many features are available to track the
object robustly with three main features which include 1) identification
of an object by gray scale value, 2) histogram of oriented gradients (HOG) and 3) local binary pattern (LBP). We propose two more powerful features namely gray
level co-occurrence matrix (GLCM) and Gaber feature for more accurate
and authenticate tracking result. To combine and process the corresponding SVMs
obtained from each features, a new collaborative strategy is developed on the
basis of the confidence distribution of the video samples which are weighted by
entropy method. We have adopted subspace evolution strategy for reconstructing
the image of the object by constructing an update model. Also, we determine
reconstruction error from the samples and again automatically build an update
model for the target which is tracked in the video sequences. Considering the
movement of the targeted object, occlusion problem is considered and overcome by constructing a
collaborative model from that of appearance model and update model. Also if
update model is of discriminative model type, binary classification problem is
taken into account and overcome by collaborative model. We run the multi-view
SVM tracking method in real time with subspace evolution strategy to track and
detect the moving objects in the crowded scene accurately. As shown in the
result part, our method also overcomes the occlusion problem that occurs
frequently while objects under rotation and illumination change due to different
environmental conditions.