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.
Highlights
Tracking is defined as the process of finding the most similar object appearance
The five features named gray level, histogram of oriented gradients (HOG), local binary pattern (LBP), Gabor features and gray level co-occurrence matrix (GLCM) are obtained from these samples and they are sent to the respective SVMs to perform training
We present a novel multi-view SVM (MVS) tracking method with multiple views of SVMs
Summary
Tracking is defined as the process of finding the most similar object appearance. The objective of crowd tracking is to determine the states of the desired object in a video sequence. The initial frame [8] [9] based static appearance models are used in many object tracking process. These methods are not capable to cope up with important appearance changes. An object model is either learned offline (by using similar visual examples) or learned by using the first few frames In both the cases once the object model is generated, immediately a predefined metric model is used to determine the position in adjacent frames. Illustration of this type of tracking algorithms includes Kernel based methods [21] and appearance models [22].
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