Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Unusual event detection in crowded scenes

Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

Activity modelling in crowded environments : a soft decision approach

Probabilistic topic models have recently been used for activity analysis in video processing, due to their strong capacity to model both local activities and interactions in crowded scenes. In those applications, a video sequence is divided into a collection of uniform non-overlaping video clips, and the high dimensional continuous inputs are quantized into a bag of discrete visual words. The hard division of video clips, and hard assignment of visual words leads to problems when an activity is split over multiple clips, or the most appropriate visual word for quantization is unclear. In this paper, we propose a novel algorithm, which makes use of a soft histogram technique to compensate for the loss of information in the quantization process; and a soft cut technique in the temporal domain to overcome problems caused by separating an activity into two video clips. In the detection process, we also apply a soft decision strategy to detect unusual events.We show that the proposed soft decision approach outperforms its hard decision counterpart in both local and global activity modelling.

Activity analysis in complicated scenes using DFT coefficients of particle trajectories

Modelling activities in crowded scenes is very challenging as object tracking is not robust in complicated scenes and optical flow does not capture long range motion. We propose a novel approach to analyse activities in crowded scenes using a “bag of particle trajectories”. Particle trajectories are extracted from foreground regions within short video clips using particle video, which estimates long range motion in contrast to optical flow which is only concerned with inter-frame motion. Our applications include temporal video segmentation and anomaly detection, and we perform our evaluation on several real-world datasets containing complicated scenes. We show that our approaches achieve state-of-the-art performance for both tasks.

Textures of optical flow for real-time anomaly detection in crowds

Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Segmentation of scenes of mobile objects and demonstrable backgrounds

In this paper we present a real-time foreground–background segmentation algorithm that exploits the following observation (very often satisfied by a static camera positioned high in its environment). If a blob moves on a pixel p that had not changed its colour significantly for a few frames, then p was probably part of the background when its colour was static. With this information we are able to update differentially pixels believed to be background. This work is relevant to autonomous minirobots, as they often navigate in buildings where smart surveillance cameras could communicate wirelessly with them. A by-product of the proposed system is a mask of the image regions which are demonstrably background. Statistically significant tests show that the proposed method has a better precision and recall rates than the state of the art foreground/background segmentation algorithm of the OpenCV computer vision library.

Optimising collection of footprint evidence from crime scenes and suspects

Analysis of either footprints or footwear impressions which have been recovered from a crime scene is a well known and well accepted part of forensic investigation. When this evidence is obtained by investigating officers, comparative analysis to a suspect’s evidence may be undertaken. This can be done either by the detectives or in some cases, podiatrists with experience in forensic analysis. Frequently asked questions of a podiatrist include; “What additional information should be collected from a suspect (for the purposes of comparison), and how should it be collected?” This paper explores the answers to these and related questions based on 20 years of practical experience in the field of crime scene analysis as it relates to podiatry and forensics. Elements of normal and abnormal foot function are explored and used to explain the high degree of variability in wear patterns produced by the interaction of the foot and footwear. Based on this understanding the potential for identifying unique features of the user and correlating this to footwear evidence becomes apparent. Standard protocols adopted by podiatrists allow for more precise, reliable, and valid results to be obtained from their analysis. Complex data sets are now being obtained by investigating officers and, in collaboration with the podiatrist; higher quality conclusions are being achieved. This presentation details the results of investigations which have used standard protocols to collect and analyse footwear and suspects of recent major crimes.

Crowded Diphosphinomethane Ligands in Catalysis: [(R2PCH2PR′2-κ2P)-NiR″]+ Cations for Ethylene Polymerization without Activators

The preparation of a series of nickel dichloride complexes with bulky diphosphinomethane chelate ligands R2PCH2PR′2 is reported. Reaction with the appropriate Grignard reagent leads to the corresponding dimethyl and dibenzyl complexes. Cationic monomethyl and mono-η3-benzyl complexes are generated from these dialkyl complexes by protonation with [H(OEt2)2]+[B(3,5-(CF3)2C6H3)4]−, while the complex [(dtbpm κ2P)Ni(η3-CH(CH2Ph)Ph]+[B(3,5-(CF3)2C6H3)4]−is obtained from protonation of the Ni(0) olefin complex (dtbpm-κ2P)N(η2-trans-stilbene). Crystal structures of examples of dichlorides, dimethyl, dibenzyl, cationic methyl, and cationic η3-benzyl complexes are reported. Solutions of the cations polymerize ethylene under mild conditions and without the necessity of an activating agent, to form polyethylene having high molecular weights and low degrees of chain branching. In comparison to the Ni methyl cations, the η3-benzyl cation complexes are more stable and somewhat less active but still very efficient in C2H4 polymerization. The effect on the resulting polyethylene of varying the substituents R, R′ on the phosphine ligand has been examined, and a clear trend for longer chain PE with less branching in the presence of more bulky substituents on the diphosphine has been found. Density functional calculations have been used to examine the rapid suprafacial η3 to η3 haptotropic shift processes of the[(R2PCH2PR′2)Ni] fragment and the η3−η1 change of the coordination mode of the benzyl group required for polymerization in those cations.

Scenes, quarters and clusters : new experiments in the formation and governance of creative places in China

This thesis examines the formation and governance patterns of the social and spatial concentration of creative people and creative business in cities. It develops a typology for creative places, adding the terms 'scene' and 'quarter' to 'clusters', to fill in the literature gap of partial emphasis on the 'creative clusters' model as an organising mechanism for regional and urban policy. In this framework, a cluster is the gathering of firms with a core focus on economic benefits; a quarter is the urban milieu usually driven by a growth coalition consisting of local government, real estate agents and residential communities; and a scene is the spontaneous assembly of artists, performers and fans with distinct cultural forms. The framework is applied to China, specifically to Hangzhou – a second-tier city in central eastern China that is ambitious to become a 'national cultural and creative industries centre'. The thesis selects three cases – Ideal & Silian 166 Creative Industries Park, White-horse Lake Eco-creative City and LOFT49 Creative Industries Park – to represent scene, quarter and cluster respectively. Drawing on in-depth interviews with initiators, managers and creative professionals of these places, together with extensive documentary analysis, the thesis investigates the composition of actors, characteristics of the locality and the diversity of activities. The findings illustrate that, in China, planning and government intervention is the key to the governance of creative space; spontaneous development processes exist, but these need a more tolerant environment, a greater diversity of cultural forms and more time to develop. Moreover, the main business development model is still real estate based: this model needs to incorporate more mature business models and an enhanced IP protection system. The thesis makes a contribution to literature on economic and cultural geography, urban planning and creative industries theory. It advocates greater attention to self-management, collaborative governance mechanisms and business strategies for scenes, quarters and clusters. As intersections exist in the terms discussed, a mixed toolkit of the three models is required to advance the creative city discourse in China.

Scenes, quarters and clusters: The formation and governance of creative places in urban China

This paper presents a PhD program examining the formation and governance patterns of the social and spatial concentration of creative people and creative businesses in cities. It develops a typology for creative places, adding the terms ‘scene’ and ‘quarter’ to ‘clusters’, to fill in the literature gap of partial emphasis on the ‘creative clusters’ model as an organising mechanism for regional and urban policy. The framework is then applied to China, specifically to Hangzhou, a second-tier city in central eastern China that is ambitious to become a ‘national cultural and creative industries centre’. Drawing on in-depth interviews with initiators, managers and creative professionals from three cases selected respectively for scene, quarter and cluster, together with extensive documentary analysis, the paper investigates the composition of actors, characteristics of the locality and the diversity of activities of the three places. The findings demonstrate a convergence of the three terms. Furthermore, in China, planning and government intervention is the key to the governance of creative places; spontaneous development processes exist, but these need a more tolerant environment, a greater diversity of cultural forms and more time to develop. Moreover, the main business development model is still real estate based: this model needs to incorporate more mature business models and an enhanced IP protection system. Finally, the business strategies need to be combined with a self-management model for the creative class, and a collaborative governance mechanism with other stakeholders such as government, real estate developers and education providers.

Why stagnant? behind the scenes in Indonesia’s reformed state asset management policies

This study seeks to answer the question of “why is policy innovation in Indonesia, in particular reformed state asset management laws and regulations, stagnant?” through an empirical and qualitative approach, identifying and exploring potential impeding influences to the full and equal implementation of said laws and regulations. The policies and regulations governing the practice of state asset management has emerged as an urgent question among many countries worldwide (Conway, 2006; Dow, Gillies, Nichols, & Polen, 2006; Kaganova, McKellar, & Peterson, 2006; McKellar, 2006b) for there is heightened awareness of the complex and crucial role that state assets play in public service provision. Indonesia is an example of such country, introducing a ‘big-bang’ reform in state asset management laws, policies, regulations, and technical guidelines. Two main reasons propelled said policy innovation: a) world-wide common challenges in state asset management practices - such as incomplete information system, accountability, and governance adherence/conceptualisation (Kaganova, McKellar and Peterson 2006); and b) unfavourable state assets audit results in all regional governments across Indonesia. The latter reasoning is emphasised, as the Indonesian government admits to past neglect in ensuring efficiency and best practice in its state asset management practices. Prior to reform there was euphoria of building and developing state assets and public infrastructure to support government programs of the day. Although this euphoria resulted in high growth within Indonesia, there seems to be little attention paid to how state assets bought/built is managed. Up until 2003-2004 state asset management is considered to be minimal; inventory of assets is done manually, there is incomplete public sector accounting standards, and incomplete financial reporting standards (Hadiyanto 2009). During that time transparency, accountability, and maintenance state assets was not the main focus, be it by the government or the society itself (Hadiyanto 2009). Indonesia exemplified its enthusiasm in reforming state asset management policies and practices through the establishment of the Directorate General of State Assets in 2006. The Directorate General of State Assets have stressed the new direction that it is taking state asset management laws and policies through the introduction of Republic of Indonesia Law Number 38 Year 2008, which is an amended regulation overruling Republic of Indonesia Law Number 6 Year 2006 on Central/Regional Government State Asset Management (Hadiyanto, 2009c). Law number 38/2008 aims to further exemplify good governance principles and puts forward a ‘the highest and best use of assets’ principle in state asset management (Hadiyanto, 2009a). The methodology of this study is that of qualitative case study approach, with a triangulated data collection method of document analysis (all relevant state asset management laws, regulations, policies, technical guidelines, and external audit reports), semi-structured interviews, and on-site observation. Empirical data of this study involved a sample of four Indonesian regional governments and 70 interviews, performed during January-July 2010. The analytical approach of this study is that of thematic analysis, in an effort to identify common influences and/or challenges to policy innovation within Indonesia. Based on the empirical data of this study specific impeding influences to state asset management reform is explored, answering the question why innovative policy implementation is stagnant. An in-depth analysis of each influencing factors to state asset management reform, and the attached interviewee’s opinions for each factor, suggests the potential of an ‘excuse rhetoric’; whereby the influencing factors identified are a smoke-screen, or are myths that public policy makers and implementers believe in; as a means to explain innovative policy stagnancy. This study offers insights to Indonesian policy makers interested in ensuring the conceptualisation and full implementation of innovative policies, particularly, although not limited to, within the context of state asset management practices.