Multi-scale Crowd Feature Detection using Vision Sensing and Statistical Mechanics Principles

Crowd behaviour analysis using vision has been subject to many different approaches. Multi-purpose crowd descriptors are one of the more recent approaches. These descriptors provide an opportunity to compare and categorise various types of crowds as well as classify their respective behaviours. Nevertheless, the automated calculation of descriptors which are expressed as measurements with accurate interpretation is a challenging problem. In this paper, analogies between human crowds and molecular thermodynamics systems are drawn for the measurement of crowd behaviour. Specifically, a novel descriptor is defined and measured for crowd behaviour at multiple scales. This descriptor uses the concept of Entropy for evaluating the state of crowd disorder. By results, the descriptor Entropy does indeed appear to capture the desired outcome for crowd entropy while utilizing easily detectable image features. Our new approach for machine understanding of crowd behaviour is promising, while it offers new complementary capabilities to the existing crowd descriptors, for example, as will be demonstrated, in the case of spectator crowds. The scope and performance of this descriptor is further discussed in details in this paper

Exploring the Revenue Mix of Nonprofit Organizations -- Does it Relate to Publicness

Nonprofit organizations offer a wide range of goods and services and seek funding from a variety of revenue sources. Our working theory n this paper is that the sources of funding are related to the services a nonprofit provides - specifically whether services are public, private, or mixed in the nature of their benefits. Using multiple subfields from three major fields in the National Taxonomy of Exempt Entities (NTEE), this study divides nonprofits according to service type, and estimates the impact of service character on particular revenue streams and overall level of revenue diversification. Generally, the proportion of revenues generated by program fees is lowest for the category deemed public, highest for those with mostly private benefits, and midway for "mixed" services which are private in character but entail substantial public benefits. Similarly, the more public a nonprofit's services, the greater the proportion of revenues it generates through donations. However, we also identify some puzzling results that suggest the need for continued investigation of the determinants of the sources and mixes of nonprofit income. Working Paper 07-3

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering

Detecting Social Network Groups from Video data acquired from surveillance cameras is a challenging problem currently being addressed by the Data Mining and Computer Vision Communities. As a part of continuing research in this area, a new graph-based post analysis approach is developed to process data obtained from the state-of-the-art Detection and Tracking systems to extract the various social groups present in it. The process of extracting social network groups is primarily divided into two tasks. The first task consists of finding a method to compute a graph that connects all the people present in the video. Motion similarity between the tracks of the people on the ground plane is used as a metric to compute the weights on the edges of the graph. The second task is to cut the graph to form groups which is done by creating a minimal spanning tree and cutting the edges with least weights. The number of cuts to be made depends on the number of groups that are present in the video. To deal with the problem of unknown number of groups, the parameter of consistency of within cluster distances is exploited and the number of groups is decided by the finding the elbow point in the plot. The method shows promising results with UCLA Courtyard Dataset Videos and Simulation systems. This work can be regarded as one of the many approaches to solve the problem of “Detecting Social Networks from Video Data” which tend to exhibit decent outcomes.Computer Science, Department o