Brock Parking

View of the Brock Parking lot and construction of the regional building in the background.

Tailgating, golf course parking, 1974

[photo taken from Pioneer High School parking lot]

Exterior view of Hauptsynagoge; Herzog Maxstrasse, Munich Synagogues

From donor letter: This is a photograph of the main synagogue of Munich, Germany. It was razed, stone by stone, in 1937 to establish a parking lot. I attended this synagogue as a child, with my parents and younger brother, until it was destroyed. Signed Melly (Engelberg) Resnicow.

Determinação de compostos monoaromáticos voláteis provenientes de emissões de veículos leves, em estacionamentos subterrâneos.

Este trabalho visa determinar a contribuição das emissões evaporativas provenientes dos veículos leves de passageiro, para a degradação da qualidade do ar atmosférico. O objetivo principal é avaliar as concentrações compostos monoaromáticos voláteis Benzeno, Tolueno, Etilbenzeno e Xilenos (BTEX) em ambientes confinados, sendo este realizado em um local que caracterize a realidade da frota veicular da Região metropolitana do Rio de Janeiro. As amostras foram coletadas em um estacionamento subterrâneo de um Shopping Center da zona norte do Rio de Janeiro, através do sistema de amostragem ativa, utilizando cartucho de carvão ativo como adsorvente. As amostras foram extraídas com solvente orgânico e analisadas posteriormente por Cromatografia gasosa acoplada à espectrometria de massas (CGEM). As médias dos resultados obtidos foram 52,7 g.m-3 para o benzeno, 203,6 g.m-3 para o tolueno, 44,6 g.m-3 para o etilbenzeno, 115,7 g.m-3 para os xilenos, sendo o tolueno o composto encontrado em maior abundância. Esses resultados foram comparados com resultados encontrados na literatura de emissões veiculares em ambientes confinados como garagens e túneis. Foi investigada a correlação com as emissões do veículo em movimento, obtidas através de estudos previamente realizados em um túnel de grande circulação e as emissões obtidas no estacionamento subterrâneo. Através desses dados ficou demonstrada diferença das fontes de emissão.

Layered Graphical Models for Tracking Partially-Occluded Objects

Partial occlusions are commonplace in a variety of real world computer vision applications: surveillance, intelligent environments, assistive robotics, autonomous navigation, etc. While occlusion handling methods have been proposed, most methods tend to break down when confronted with numerous occluders in a scene. In this paper, a layered image-plane representation for tracking people through substantial occlusions is proposed. An image-plane representation of motion around an object is associated with a pre-computed graphical model, which can be instantiated efficiently during online tracking. A global state and observation space is obtained by linking transitions between layers. A Reversible Jump Markov Chain Monte Carlo approach is used to infer the number of people and track them online. The method outperforms two state-of-the-art methods for tracking over extended occlusions, given videos of a parking lot with numerous vehicles and a laboratory with many desks and workstations.

Three Hundred Years in Annapolis: Phase III Archaeological Investigations of the Anne Arundel County Courthouse Site (18AP63), Annapolis, Maryland

During the summer of 1994, Archaeology in Annapolis conducted archaeological investigations of the city block bounded by Franklin, South and Cathedral Streets in the city of Annapolis. This Phase III excavation was conducted as a means to identify subsurface cultural resources in the impact area associated with the proposed construction of the Anne Arundel County Courthouse addition. This impact area included both the upper and lower parking lots used by Courthouse employees. Investigations were conducted in the form of mechanical trenching and hand excavated units. Excavations in the upper lot area yielded significant information concerning the interior area of the block. Known as Bellis Court, this series of rowhouses was constructed in the late nineteenth century and was used as rental properties by African-Americans. The dwellings remained until the middle of the twentieth century when they were demolished in preparation for the construction of a Courthouse addition. Portions of the foundation of a house owned by William H. Bellis in the 1870s were also exposed in this area. Construction of this house was begun by William Nicholson around 1730 and completed by Daniel Dulany in 1732/33. It was demolished in 1896 by James Munroe, a Trustee for Bellis. Excavations in the upper lot also revealed the remains of a late seventeenth/early eighteenth century wood-lined cellar, believed to be part of the earliest known structure on Lot 58. After an initially rapid deposition of fill around 1828, this cellar was gradually covered with soil throughout the remainder of the nineteenth century. The fill deposit in the cellar feature yielded a mixed assemblage of artifacts that included sherds of early materials such as North Devon gravel-tempered earthenware, North Devon sgraffito and Northem Italian slipware, along with creamware, pearlware and whiteware. In the lower parking lot, numerous artifacts were recovered from yard scatter associated with the houses that at one time fronted along Cathedral Street and were occupied by African- Americans. An assemblage of late seventeenth century/early eighteenth century materials and several slag deposits from an early forge were recovered from this second area of study. The materials associated with the forge, including portions of a crucible, provided evidence of some of the earliest industry in Annapolis. Investigations in both the upper and lower parking lots added to the knowledge of the changing landscape within the project area, including a prevalence of open space in early periods, a surprising survival of impermanent structures, and a gradual regrading and filling of the block with houses and interior courts. Excavations at the Anne Arundel County Courthouse proved this to be a multi-component site, rich in cultural resources from Annapolis' Early Settlement Period through its Modern Period (as specified by Maryland's Comprehensive Historic Preservation Plan (Weissman 1986)). This report provides detailed interpretations of the archaeological findings of these Phase III investigations.

Archaeological Excavations at 18AP44: 193 Main Street, Annapolis, Maryland, 1985-1987

193 Main Street (18AP44) is located between Main Street and Duke of Gloucester Street. The property was used ass a yard related to residential and commercial buildings during the 18th and 19th centuries. In the 1930's a movie theatre and parking lot were built on the property. That structure was torn down in the 1980's and a three-story commercial building was constructed. Archaeological excavations were conducted on the property from 1985-1987. A preliminary report was written in 1986 by Paul A. Shackel. This report is the final report on the archaeological investigations at 193 Main Street.

Academic Staging Building

A view from the main parking lot near the time of completion of the complex.

Phase 1 Map

Phase 1 Map of the Master Plan showing the Tower and Thistle Complex, along with some additional buildings and the main parking lot. Note the location of the Central Utilities Building to the east differs from it's actual location along the escarpment.

Welch Hall Overhead

This view from the Twer facing southwest shows the College of Education, now Welch Hall, and the former parking lot situated west of it (behind it).

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context.

A unified approach to the recognition of complex actions from sequences of zone-crossings

We present a method for the recognition of complex actions. Our method combines automatic learning of simple actions and manual definition of complex actions in a single grammar. Contrary to the general trend in complex action recognition that consists in dividing recognition into two stages, our method performs recognition of simple and complex actions in a unified way. This is performed by encoding simple action HMMs within the stochastic grammar that models complex actions. This unified approach enables a more effective influence of the higher activity layers into the recognition of simple actions which leads to a substantial improvement in the classification of complex actions. We consider the recognition of complex actions based on person transits between areas in the scene. As input, our method receives crossings of tracks along a set of zones which are derived using unsupervised learning of the movement patterns of the objects in the scene. We evaluate our method on a large dataset showing normal, suspicious and threat behaviour on a parking lot. Experiments show an improvement of ~ 30% in the recognition of both high-level scenarios and their composing simple actions with respect to a two-stage approach. Experiments with synthetic noise simulating the most common tracking failures show that our method only experiences a limited decrease in performance when moderate amounts of noise are added.

PETS 2015: datasets and challenge

This paper presents the two datasets (ARENA and P5) and the challenge that form a part of the PETS 2015 workshop. The datasets consist of scenarios recorded by us- ing multiple visual and thermal sensors. The scenarios in ARENA dataset involve different staged activities around a parked vehicle in a parking lot in UK and those in P5 dataset involve different staged activities around the perimeter of a nuclear power plant in Sweden. The scenarios of each dataset are grouped into ‘Normal’, ‘Warning’ and ‘Alarm’ categories. The Challenge specifically includes tasks that account for different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis (‘atomic’ event detection) and High-Level Video Analysis (‘complex’ event detection). The evaluation methodology used for the Challenge includes well-established measures.

PETS 2014: dataset and challenge

This paper describes the dataset and vision challenges that form part of the PETS 2014 workshop. The datasets are multisensor sequences containing different activities around a parked vehicle in a parking lot. The dataset scenarios were filmed from multiple cameras mounted on the vehicle itself and involve multiple actors. In PETS2014 workshop, 22 acted scenarios are provided of abnormal behaviour around the parked vehicle. The aim in PETS 2014 is to provide a standard benchmark that indicates how detection, tracking, abnormality and behaviour analysis systems perform against a common database. The dataset specifically addresses several vision challenges corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis (‘simple’ event detection: the behaviour recognition of a single actor) and High-Level Video Analysis (‘complex’ event detection: the behaviour and interaction recognition of several actors).