Application of the self-organising map to trajectory classification

This paper presents an approach to the problem of automatically classifying events detected by video surveillance systems; specifically, of detecting unusual or suspicious movements. Approaches to this problem typically involve building complex 3D-models in real-world coordinates to provide trajectory information for the classifier. In this paper we show that analysis of trajectories may be carried out in a model-free fashion, using self-organising feature map neural networks to learn the characteristics of normal trajectories, and to detect novel ones. Trajectories are represented using positional and first and second order motion information, with moving-average smoothing. This allows novelty detection to be applied on a point-by-point basis in real time, and permits both instantaneous motion and whole trajectory motion to be subjected to novelty detection

A Neural System for Automated CCTV Surveillance

This paper overviews a new system, the “Owens Tracker,” for automated identification of suspicious pedestrian activity in a car-park. Centralized CCTV systems relay multiple video streams to a central point for monitoring by an operator. The operator receives a continuous stream of information, mostly related to normal activity, making it difficult to maintain concentration at a sufficiently high level. While it is difficult to place quantitative boundaries on the number of scenes and time period over which effective monitoring can be performed, Wallace and Diffley [1] give some guidance, based on empirical and anecdotal evidence, suggesting that the number of cameras monitored by an operator be no greater than 16, and that the period of effective monitoring may be as low as 30 minutes before recuperation is required. An intelligent video surveillance system should therefore act as a filter, censuring inactive scenes and scenes showing normal activity. By presenting the operator only with unusual activity his/her attention is effectively focussed, and the ratio of cameras to operators can be increased. The Owens Tracker learns to recognize environmentspecific normal behaviour, and refers sequences of unusual behaviour for operator attention. The system was developed using standard low-resolution CCTV cameras operating in the car-parks of Doxford Park Industrial Estate (Sunderland, Tyne and Wear), and targets unusual pedestrian behaviour. The modus operandi of the system is to highlight excursions from a learned model of normal behaviour in the monitored scene. The system tracks objects and extracts their centroids; behaviour is defined as the trajectory traced by an object centroid; normality as the trajectories typically encountered in the scene. The essential stages in the system are: segmentation of objects of interest; disambiguation and tracking of multiple contacts, including the handling of occlusion and noise, and successful tracking of objects that “merge” during motion; identification of unusual trajectories. These three stages are discussed in more detail in the following sections, and the system performance is then evaluated

Novelty detection in video surveillance using hierarchical neural networks

Abstract. A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections, modified so that higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding “novelty accumulator” nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state

Deregulation and efficiency: the case of private Korean banks

This paper examines the productive efficiency of a sample of private Korean banks over the 1985 to 1995 time period. The goal of the analysis is to identify the key determinants of Korean bank efficiency (inefficiency) following the program of deregulation initiated by the government in the early 1980s and augmented in the early 1990s. Using the stochastic frontier cost function approach, efficiency scores were determined for each bank in the sample. A second stage efficiency regression was then estimated to identify the key determinants of operating efficiency. In general, the results show that banks with higher rates of asset growth, fewer employees per million won of assets, larger amounts of core deposits, and lower expense ratios were more efficient. In addition, banks which branched nationwide were found to be more efficient. The financial deregulation of 1991 was found to have had little or no significant effect on the level of sample bank efficiency.Banks and banking - Korea ; Korea

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

Proteomics of Carbon Fixation Energy Sources in Halothiobacillus neapolitanus

Through the use of proteomics, it was uncovered that the autotrophic, aerobic purple sulfur bacterium Halothiobacillus neapolitanus displays changes in cellular levels of portions of its carbon dioxide uptake and fixation mechanisms upon switch from bicarbonate to CO2(g) as carbon source. This includes an increase in level of a heterodimeric bicarbonate transporter along with a potential switch between form I and form II of RubisCO. Additional changes are seen in several sulfur oxidation pathways, which may indicate a link between sulfur oxidation pathways as an energy source and carbon uptake/fixation mechanisms

Jurassic and Cretaceous tectonic evolution of the southeast Castle Dome Mountains, southwest Arizona

The southeast Castle Dome Mountains of southwest Arizona record the Mesozoic to Cenozoic geologic evolution of the Southwest. The age and tectonic setting of the informally named metasedimentary and metavolcanic rocks of Slumgullion have occasioned considerable debate, although some previous researchers have postulated a Jurassic age. Possible correlatives to the rocks of Slumgullion include the McCoy Mountains Formation of southeast California and southwest Arizona and the Winterhaven Formation of southeast California. Within the rocks of Slumgullion detrital zircon ages were obtained from various metasedimentary units, including quartz arenites, arkosic sandstones, and a lithic arenite, in order to place constraints on depositional age and provenance. Primary igneous zircon ages were also determined for a metadacite from the base of the rocks of Slumgullion and from a monzogranite, which previous workers interpreted as having intruded the rocks of Slumgullion. Ages from U-Pb zircon dating reveal the base of the Slumgullion may be Jurassic (youngest zircon ca. 160 Ma), but the top of the sequence is no older than latest Cretaceous (three 78-77 Ma zircons). The metadacite and monzogranite appear to be older than the metasediments and likely represent the depositional basement to the Slumgullion sedimentary basin. After deposition of the latest Cretaceous Slumgullion unit, the monzogranite (158 Ma average crystallization age) was faulted over the former. Age peaks on probability density plots show that quartz arenites from the rocks of Slumgullion have similar source regions as the basal McCoy Mountains Formation and the Winterhaven Formation. This age signature is also similar to that of the Jurassic ergs from the Colorado Plateau. The quartz arenites within the rocks of Slumgullion thus indicate the derivation of sediment from relatively distant source regions. The less mature sandstones in the rocks of Slumgullion represent a different history as indicated by the fact that their sediment is locally derived. The Orocopia Schist, which is widely viewed as a subduction complex, was also studied from the Castle Dome Mountains. U-Pb detrital zircon data obtained from two quartzofeldspathic samples of the schist are consistent with previous results implying a depositional age of Latest Cretaceous-earliest Paleogene. Results from 40Ar/39Ar thermochronology are also consistent with previous studies. Muscovite ages of ca. 42 Ma from both the Orocopia Schist and upper plate gneiss indicate that slip had occurred on the Chocolate Mountains fault system by this time. There is an ~20 m.y. difference in biotite 40Ar/39Ar ages between the Orocopia Schist and gneiss, but that can be explained by reheating due to Miocene volcanism

Deep learning-based anomaly detection for edge-layer devices

This thesis work proposes a novel DL-based anomaly detection framework for IoT environments, employing higher-capacity embedded devices as a first line of defense for the IoT edge layer. In the proposed framework, embedded devices implement the DL anomaly detection engine at the network gateway and adapt to potential attacks by retraining on incoming network traffic. In order to test the feasibility of this framework, two neural network models, trained on variations of the CICIDS 2018 Intrusion Detection Data Set, are deployed and tested on the Raspberry Pi 4. Model performance metrics, including fit and evaluation time across various batch and data sizes, are compared alongside those of identical models running on higher-capacity devices. Device resource metrics of CPU and Memory usage are monitored for comparison across model variations, batch and data sizes.The potential benefit of retraining models at the edge is evaluated by comparing performance of models executing consistent retraining