Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Recognising and monitoring high-level behaviours in complex spatial environments

The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. This paper presents a system for recognising high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviours. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviours of people from observing their trajectories within a real, complex indoor environment.

Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.

Learning people movement model from multiple cameras for behaviour recognition

In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.

Random Subspace Two-Dimensional PCA for face recognition

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets -the ORL database, the Yale face database and the extended Yale face database B - confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.

Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.

Coordination of multiple cameras to track multiple people

In this paper, we present a distributed surveillance system that uses multiple cheap static cameras to track multiple people in indoor environments. The system has a set of Camera Processing Modules and a Central Module to coordinate the tracking tasks among the cameras. Since each object in the scene can be tracked by a number of cameras, the problem is how to choose the most appropriate camera for each object. We propose a novel algorithm to allocate objects to cameras using the object-to-camera distance while taking into account occlusion. The algorithm attempts to assign objects in the overlapping fields of view to the nearest camera which can see the object without occlusion. Experimental results show that the system can coordinate cameras to track people properly and can deal well with occlusion.

Discovery of activity structures using the hierarchical hidden Markov model

In building a surveillance system for monitoring people behaviours, it is important to understand the typical patterns of people's movement in the environment. This task is difficult when dealing with high-level behaviours. The flat model such as the hidden Markov model (HMM) is inefficient in differentiating between signatures of such behaviours. This paper examines structure learning for high-level behaviours using the hierarchical hidden Markov model (HHMM).We propose a two-phase learning algorithm in which the parameters of the behaviours at low levels are estimated first and then the structures and parameters of the behaviours at high levels are learned from multi-camera training data. Our algorithm is then evaluated using data from a real environment, demonstrating the robustness of the learned structure in recognising people's behaviour.

EST-PAC HPC - a web portal for high-throughput EST annotation and protein sequence prediction

Expressed Sequence Tags (ESTs) are short DNA sequences generated by sequencing the transcribed cDNAs coming from a gene expression. They can provide significant functional, structural and evolutionary information and thus are a primary resource for gene discovery. EST annotation basically refers to the analysis of unknown ESTs that can be performed by database similarity search for possible identities and database search for functional prediction of translation products. Such kind of annotation typically consists of a series of repetitive tasks which should be automated, and be customizable and amenable to using distributed computing resources. Furthermore, processing of EST data should be done efficiently using a high performance computing platform. In this paper, we describe an EST annotator, EST-PACHPC, which has been developed for harnessing HPC resources potentially from Grid and Cloud systems for high throughput EST annotations. The performance analysis of EST-PACHPC has shown that it provides substantial performance gain in EST annotation.

Simplifying gene expression microarray comparative analysis.

Gene Expression Comparative Analysis allows bioinformatics researchers to discover the conserved or specific functional regulation of genes. This is achieved through comparisons between quantitative gene expression measurements obtained in different species on different platforms to address a particular biological system. Comparisons are made more difficult due to the need to map orthologous genes between species, pre-processing of data (normalization) and post-analysis (statistical and correlation analysis). In this paper we introduce a web-based software package called EXP-PAC which provides on line interfaces for database construction and query of data, and makes use of a high performance computing platform of computer clusters to run gene sequence mapping and normalization methods in parallel. Thus, EXP-PAC facilitates the integration of gene expression data for comparative analysis and the online sharing, retrieval and visualization of complex multi-specific and multi-platform gene expression results.

Transcriptional insights on the regenerative mechanics of axotomized neurons in vitro

Axotomized neurons have the innate ability to undergo regenerative sprouting but this is often impeded by the inhibitory central nervous system environment. To gain mechanistic insights into the key molecular determinates that specifically underlie neuronal regeneration at a transcriptomic level, we have undertaken a DNA microarray study on mature cortical neuronal clusters maintained in vitro at 8, 15, 24 and 48 hrs following complete axonal severance. A total of 305 genes, each with a minimum fold change of ±1.5 for at least one out of the four time points and which achieved statistical significance (one-way ANOVA, P < 0.05), were identified by DAVID and classified into 14 different functional clusters according to Gene Ontology. From our data, we conclude that post-injury regenerative sprouting is an intricate process that requires two distinct pathways. Firstly, it involves restructuring of the neurite cytoskeleton, determined by compound actin and microtubule dynamics, protein trafficking and concomitant modulation of both guidance cues and neurotrophic factors. Secondly, it elicits a cell survival response whereby genes are regulated to protect against oxidative stress, inflammation and cellular ion imbalance. Our data reveal that neurons have the capability to fight insults by elevating biological antioxidants, regulating secondary messengers, suppressing apoptotic genes, controlling ion-associated processes and by expressing cell cycle proteins that, in the context of neuronal injury, could potentially have functions outside their normal role in cell division. Overall, vigilant control of cell survival responses against pernicious secondary processes is vital to avoid cell death and ensure successful neurite regeneration.

Strong population genetic structure and its management implications in the mud carp Cirrhinus molitorella, an indigenous freshwater species subject to an aquaculture and culture-based fishery

This study investigated population genetic structure and diversity of mud carp Cirrhinus molitorella, a species widely used in aquaculture and culture-based fisheries in China and Mekong River riparian countries. Seven newly developed and one published microsatellite DNA markers were used to analyse samples from six wild locations, four hatchery broodstocks and one farmed site from the Mekong, Red and Pearl Rivers. Significant genetic structure was detected in C. molitorella, with isolation-by-distance being a strong force in the Mekong. Pair-wise F_ST, Fisher's exact tests for population differentiation, permutation tests and individual-based structure analysis all support the recognition of a sample originating from Toul Krasaing Lake (Cambodia) and one between Kratie and Stung Treng (Cambodia) as distinct from the remainder of the sampled range. Samples from the main upper Mekong and the Nam Khan River were significantly differentiated, but on a time scale inferred to be short (i.e. by genetic drift, not sufficient for evolution of new microsatellite alleles). The Mekong stock of C. molitorella was strongly differentiated from those from the Red and Pearl Rivers, inferred to be on an evolutionary time scale. Finer-scale sampling is warranted to further improve the understanding of genetic interactions among fish from the Mekong and its tributaries. Detailed studies on the ecology of C. molitorella (e.g. migration pathways and preferred spawning habitats) would provide useful information to explain the patterns of genetic structure detected here, and deepen insights about evolutionary distinctiveness of the population units.

Multifaceted role of nitric oxide in an in vitro mouse neuronal injury model: transcriptomic profiling defines the temporal recruitment of death signalling cascades

Nitric oxide is implicated in the pathogenesis of various neuropathologies characterized by oxidative stress. Although nitric oxide has been reported to be involved in the exacerbation of oxidative stress observed in several neuropathologies, existent data fail to provide a holistic description of how nitrergic pathobiology elicits neuronal injury. Here we provide a comprehensive description of mechanisms contributing to nitric oxide induced neuronal injury by global transcriptomic profiling. Microarray analyses were undertaken on RNA from murine primary cortical neurons treated with the nitric oxide generator DETA-NONOate (NOC-18, 0.5 mM) for 8–24 hrs. Biological pathway analysis focused upon 3672 gene probes which demonstrated at least a ±1.5-fold expression in a minimum of one out of three time-points and passed statistical analysis (one-way anova, P < 0.05). Numerous enriched processes potentially determining nitric oxide mediated neuronal injury were identified from the transcriptomic profile: cell death, developmental growth and survival, cell cycle, calcium ion homeostasis, endoplasmic reticulum stress, oxidative stress, mitochondrial homeostasis, ubiquitin-mediated proteolysis, and GSH and nitric oxide metabolism. Our detailed time-course study of nitric oxide induced neuronal injury allowed us to provide the first time a holistic description of the temporal sequence of cellular events contributing to nitrergic injury. These data form a foundation for the development of screening platforms and define targets for intervention in nitric oxide neuropathologies where nitric oxide mediated injury is causative.