STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

Fast human detection for video event recognition

Human body detection, which has become a research hotspot during the last two years, can be used in many video content analysis applications. This paper investigates a fast human detection method for volume based video event detection. Compared with other object detection systems, human body detection brings more challenge due to threshold problems coming from a wide range of dynamic properties. Motivated by approaches successfully introduced in facial recognition applications, it adapts and adopts feature extraction and machine learning mechanism to classify certain areas from video frames. This method starts from the extraction of Haar-like features from large numbers of sample images for well-regulated feature distribution and is followed by AdaBoost learning and detection algorithm for pattern classification. Experiment on the classifier proves the Haar-like feature based machine learning mechanism can provide a fast and steady result for human body detection and can be further applied to reduce negative aspects in human modelling and analysis for volume based event detection

限られた学習データ下の画像セグメンテーション：実用的なドメイン適応と少数事例学習

Tohoku University博士（情報科学）thesi

限られた学習データ下の画像セグメンテーション：実用的なドメイン適応と少数事例学習

othe

Accurate gradient computations at interfaces using finite element methods

New finite element methods are proposed for elliptic interface problems in one and two dimensions. The main motivation is not only to get an accurate solution but also an accurate first order derivative at the interface (from each side). The key in 1D is to use the idea from \cite{wheeler1974galerkin}. For 2D interface problems, the idea is to introduce a small tube near the interface and introduce the gradient as part of unknowns, which is similar to a mixed finite element method, except only at the interface. Thus the computational cost is just slightly higher than the standard finite element method. We present rigorous one dimensional analysis, which show second order convergence order for both of the solution and the gradient in 1D. For two dimensional problems, we present numerical results and observe second order convergence for the solution, and super-convergence for the gradient at the interface

Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection

With the rapidly increasing demands from surveillance and security industries, crowd behaviour analysis has become one of the hotly pursued video event detection frontiers within the computer vision arena in recent years. This research has investigated innovative crowd behaviour detection approaches based on statistical crowd features extracted from video footages. In this paper, a new crowd video anomaly detection algorithm has been developed based on analysing the extracted spatio-temporal textures. The algorithm has been designed for real-time applications by deploying low-level statistical features and alleviating complicated machine learning and recognition processes. In the experiments, the system has been proven a valid solution for detecting anomaly behaviours without strong assumptions on the nature of crowds, for example, subjects and density. The developed prototype shows improved adaptability and efficiency against chosen benchmark systems

A graph theory-based online keywords model for image semantic extraction

Image captions and keywords are the semantic descriptions of the dominant visual content features in a targeted visual scene. Traditional image keywords extraction processes involves intensive data- and knowledge-level operations by using computer vision and machine learning techniques. However, recent studies have shown that the gap between pixel-level processing and the semantic definition of an image is difficult to bridge by counting only the visual features. In this paper, augmented image semantic information has been introduced through harnessing functions of online image search engines. A graphical model named as the “Head-words Relationship Network” (HWRN) has been devised for tackling the aforementioned problems. The proposed algorithm starts from retrieving online images of similarly visual features from the input image, the text content of their hosting webpages are then extracted, classified and analysed for semantic clues. The relationships of those “head-words” from relevant webpages can then be modelled and quantified using linguistic tools. Experiments on the prototype system have proven the effectiveness of this novel approach. Performance evaluation over benchmarking state-of-the-art approaches has also shown satisfactory results and promising future applications