Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D

An entropy model for loiterer retrieval across multiple surveillance cameras

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates

An entropy model for loiterer retrieval across multiple surveillance cameras

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates

Critical Asset and Portfolio Risk Analysis for Homeland Security

Providing a defensible basis for allocating resources for critical infrastructure and key resource protection is an important and challenging problem. Investments can be made in countermeasures that improve the security and hardness of a potential target exposed to a security hazard, deterrence measures to decrease the likeliness of a security event, and capabilities to mitigate human, economic, and other types of losses following an incident. Multiple threat types must be considered, spanning everything from natural hazards, industrial accidents, and human-caused security threats. In addition, investment decisions can be made at multiple levels of abstraction and leadership, from tactical decisions for real-time protection of assets to operational and strategic decisions affecting individual assets and assets comprising a regions or sector. The objective of this research is to develop a probabilistic risk analysis methodology for critical asset protection, called Critical Asset and Portfolio Risk Analysis, or CAPRA, that supports operational and strategic resource allocation decisions at any level of leadership or system abstraction. The CAPRA methodology consists of six analysis phases: scenario identification, consequence and severity assessment, overall vulnerability assessment, threat probability assessment, actionable risk assessment, and benefit-cost analysis. The results from the first four phases of CAPRA combine in the fifth phase to produce actionable risk information that informs decision makers on where to focus attention for cost-effective risk reduction. If the risk is determined to be unacceptable and potentially mitigable, the sixth phase offers methods for conducting a probabilistic benefit-cost analysis of alternative risk mitigation strategies. Several case studies are provided to demonstrate the methodology, including an asset-level analysis that leverages systems reliability analysis techniques and a regional-level portfolio analysis that leverages techniques from approximate reasoning. The main achievements of this research are three-fold. First, this research develops methods for security risk analysis that specifically accommodates the dynamic behavior of intelligent adversaries, to include their tendency to shift attention toward attractive targets and to seek opportunities to exploit defender ignorance of plausible targets and attack modes to achieve surprise. Second, this research develops and employs an expanded definition of vulnerability that takes into account all system weaknesses from initiating event to consequence. That is, this research formally extends the meaning of vulnerability beyond security weaknesses to include target fragility, the intrinsic resistance to loss of the systems comprising the asset, and weaknesses in response and recovery capabilities. Third, this research demonstrates that useful actionable risk information can be produced even with limited information supporting precise estimates of model parameters

Maritime expressions:a corpus based exploration of maritime metaphors

This study uses a purpose-built corpus to explore the linguistic legacy of Britain’s maritime history found in the form of hundreds of specialised ‘Maritime Expressions’ (MEs), such as TAKEN ABACK, ANCHOR and ALOOF, that permeate modern English. Selecting just those expressions commencing with ’A’, it analyses 61 MEs in detail and describes the processes by which these technical expressions, from a highly specialised occupational discourse community, have made their way into modern English. The Maritime Text Corpus (MTC) comprises 8.8 million words, encompassing a range of text types and registers, selected to provide a cross-section of ‘maritime’ writing. It is analysed using WordSmith analytical software (Scott, 2010), with the 100 million-word British National Corpus (BNC) as a reference corpus. Using the MTC, a list of keywords of specific salience within the maritime discourse has been compiled and, using frequency data, concordances and collocations, these MEs are described in detail and their use and form in the MTC and the BNC is compared. The study examines the transformation from ME to figurative use in the general discourse, in terms of form and metaphoricity. MEs are classified according to their metaphorical strength and their transference from maritime usage into new registers and domains such as those of business, politics, sports and reportage etc. A revised model of metaphoricity is developed and a new category of figurative expression, the ‘resonator’, is proposed. Additionally, developing the work of Lakov and Johnson, Kovesces and others on Conceptual Metaphor Theory (CMT), a number of Maritime Conceptual Metaphors are identified and their cultural significance is discussed