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Recently, many works have been published for counting people. However, when being applied to real-world train station videos, they have exposed many limitations due to problems such as low resolution, heavy occlusion, various density levels and perspective distortions. In this paper, following the recent trend of regression-based density estimation, we present a linear regression approach based on...
This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation...
Recent progress in sketch-based 3D shape retrieval creates a novel and user-friendly way to explore massive 3D shapes on the Internet. However, current methods on this topic rely on designing invariant features for both sketches and 3D shapes, or complex matching strategies. Therefore, they suffer from problems like arbitrary drawings and inconsistent viewpoints. To tackle this problem, we propose...
In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors...
Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns...
Multi-instance learning (MIL) is a variational supervised learning. Instead of getting a set of instances that are labeled, the learner receives a set of bags that are labeled. Each bag contains many instances. In this paper, we present a novel MIL algorithm that can efficiently learn classifiers in a large instance space. We achieve this by estimating instance distribution using a proposed extended...
Anomaly Intrusion Detection System (IDS) is a statistical based network IDS which can detect attack variants and novel attacks without a priori knowledge. Current anomaly IDSs are inefficient for real-time detection because of their complex computation. This paper proposes a novel approach to reduce the heavy computational cost of an anomaly IDS. Linear Discriminant Analysis (LDA) and difference distance...
Many machine learning tasks can be achieved by using Multiple-instance learning (MIL) when the target features are ambiguous. As a general MIL framework, Diverse Density (DD) provides a way to learn those ambiguous features by maxmising the DD estimator, and the maximum of DD estimator is called a concept. However, modeling and finding multiple concepts is often difficult especially without prior...
Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale...
In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances...
A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear discriminant analysis (LDA) is applied to the set of key postures to obtain low dimensional feature vectors...
This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting...
A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics...
High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an inductive learning based method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used...
This paper proposed a method for discriminating pedestrians from rigid objects in a video. The methodis a motion-based recognition of moving objects. This method is motivated by the assumptions that human beings are non-rigid and their movements are periodic. Moving objects and their skeletons are extracted. The motion cue is determined by the angle formed by the centroid point and the two bottom...
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