Space-time Facet Model for Human Activity Classification

This paper presents a new method to detect space time interest point (STIP) from video data. We use three dimensional facet model to detect STIP and call it as facet space-time interest point or FaSTIP. The proposed algorithm detects all the desired interest points efficiently in different scales compared to other existing methods. A video clip is described as a collection of 3D wavelet base features computed at these interest points. Finally, multi-channel SVM with χ2- kernel is used to classify human actions. Our contribution here are two fold: first, we present a new algorithm for interest point detection in video data, and second, we propose a new descriptor for general human activity classification. Experimental results show the accuracy of the detected interest points and the power of descriptor compared to the state-of-the-art.

Discriminative two-level feature selection for realistic human action recognition

Fusing appearance and distribution information of interest points for action recognition

In this paper we propose a framework for activity recognition based on space-time interest point in video surveillance. Single type interest point feature is not sufficient to identify the activity therefore we have considered multi-class activities fussed in three dimensional (spatial & time) coordinate to achieve our objective with maximum accuracy. Our experiment shows that fusing multi class activity using global feature provides a copious representation of human daily action when compared to the use of a single feature type. This paper model the spatial temporal distribution of interest points for identification of various short or long duration activities. It is scalable in nature and work efficiently under conditions of dynamic background, changing camera view angle or zooming, front and sidelong activities. It assumes that human being is performing action. The experiment on two benchmark datasets show propitious results when compared with the state-of-the-art methods.

Gait has been known as an effective biometric feature to identify a person at a distance, e.g., in video surveillance applications. Many methods have been proposed for gait recognitions from various different perspectives. It is found that these methods rely on appearance (e.g., shape contour, silhouette)-based analyses, which require preprocessing of foreground–background segmentation (FG/BG). This process not only causes additional time complexity, but also adversely influences performances of gait analyses due to imperfections of existing FG/BG methods. Besides, appearance-based gait recognitions are sensitive to several variations and partial occlusions, e.g., caused by carrying a bag and varying a cloth type. To avoid these limitations, this paper proposes a new framework to construct a new gait feature directly from a raw video. The proposed gait feature extraction process is performed in the spatio-temporal domain. The space-time interest points (STIPs) are detected by considering large variations along both spatial and temporal directions in local spatio-temporal volumes of a raw gait video sequence. Thus, STIPs are allocated, where there are significant movements of human body in both space and time. A histogram of oriented gradients and a histogram of optical flow are computed on a 3D video patch in a neighborhood of each detected STIP, as a STIP descriptor. Then, the bag-of-words model is applied on each set of STIP descriptors to construct a gait feature for representing and recognizing an individual gait. When compared with other existing methods in the literature, it has been shown that the performance of the proposed method is promising for the case of normal walking, and is outstanding for the case of partial occlusion caused by walking with carrying a bag and walking with varying a cloth type.

Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.

Understanding highly accurate and real-time violent actions from surveillance videos is a demanding challenge. Our primary contribution of this work is divided into two parts. Firstly, we propose a computationally efficient Bag-of-Words (BoW) pipeline along with improved accuracy of violent videos classification. The novel pipeline's feature extraction stage is implemented with densely sampled Histogram of Oriented Gradients (HOG) and Histogram of Optical Flow (HOF) descriptors rather than Space-Time Interest Point (STIP) based extraction. Secondly, in encoding stage, we propose Outlier-Resistant VLAD (OR-VLAD), a novel higher order statistics-based feature encoding, to improve the original VLAD performance. In classification, efficient Linear Support Vector Machine (LSVM) is employed. The performance of the proposed pipeline is evaluated with three popular violent action datasets. On comparison, our pipeline achieved near perfect classification accuracies over three standard video datasets, outperforming most state-of-the-art approaches and having very low number of vocabulary size compared to previous BoW Models.

Obstacle avoidance is an important and challenging task for the autonomous flight of unmanned aerial vehicles. Obstacle regions extraction from image sequences is a critical prerequisite in obstacle avoidance. We propose an obstacle regions extraction method based on space–time tensor descriptor. In our method, first, the space–time tensor descriptor is defined and a criterion function based on the descriptor of extracting space–time interest points (STIPs) is designed. Then a self-adaptive clustering of STIPs approach is presented to locate the possible obstacle regions. Finally, an improved level set algorithm is applied with the result of clustering to extract the obstacle regions. We demonstrate the experiments of obstacle regions extraction by our method on image sequences. Sequences are captured in indoor simulative obstacle avoidance environments and outdoor real flight obstacle avoidance environments. Experimental results validate that our method can effectively complete extraction and segmentation of obstacle region with captured images. Compared with the state-of-the-art methods, our method performs well to extract the contours of obstacle regions on the whole and significantly improves segmentation speed.

A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

Analysis of fractional order Bonhoeffer–van der Pol oscillator

In this contribution, we analyze the memory effect associated with fractional derivatives in a Cournot-type oligopoly model. Such an analysis will be made from the comparison of the solutions of a duopoly with fractional derivatives of different orders. We show that the firm that has more memory on the competition, given by the fractional derivative, will have advantages over its competitor in the long run. Moreover, we show that the choices of fractional derivatives αj ∈ (0,3/5) for both firms j = 1,2 will result in advantages in profit compared to the profit of firms without memory.

<p>Two inverse source problems for a space-time fractional differential equation involving bi-fractional Laplacian operators in the spatial variable and Caputo time-fractional derivatives of different orders between 1 and 2 are studied. In the first inverse source problem, the space-dependent term along with the diffusion concentration is recovered, while in the second inverse source problem, the time-dependent term along with the diffusion concentration is identified. Both inverse source problems are ill-posed in the sense of Hadamard. The existence and uniqueness of solutions for both inverse source problems are investigated. Finally, several examples are presented to illustrate the obtained results for the inverse source problems.</p>

In this article the existence as well as the uniqueness (EU) of the solutions for nonlinear multiorder fractional-differential equations (FDE) with local boundary conditions and fractional derivatives of different orders (Caputo and Riemann–Liouville) are covered. The existence result is derived from Krasnoselskii’s fixed point theorem and its uniqueness is shown using the Banach contraction mapping principle. To illustrate the reliability of the results, two examples are given.

Algorithm ALESCAF (alternating least squares identification based on the characteristic function) uses the derivatives of the second characteristic function (c.f.) of observations, without any need of sparsity assumption on sources, but assuming their statistical independence. ALESCAF was already proposed by the authors in P. Comon and M. Rajih (2005), where only one derivative order was considered. In this paper, new versions of ALESCAF are proposed, that jointly use derivatives of different orders. We also propose ALESCAS, a new algorithm that uses the knowledge of source c.f.'s. Computer simulations demonstrate that both algorithms accelerate the convergence

In this paper, we investigate the existence of solutions for a nonlinear fractional-order coupled system involving both Caputo and Riemann–Liouville generalized fractional derivatives of different orders equipped with coupled integral boundary conditions. We transform the given system into an equivalent fixed point problem and solve it by applying the standard fixed point theorems. Examples are constructed for the illustration of the obtained results.