Visual Human Tracking and Group Activity Analysis: A Video Mining System for Retail Marketing

Thesis (PhD) - Indiana University, Computer Sciences, 2007In this thesis we present a system for automatic human tracking and activity recognition from video sequences. The problem of automated analysis of visual information in order to derive descriptors of high level human activities has intrigued computer vision community for decades and is considered to be largely unsolved. A part of this interest is derived from the vast range of applications in which such a solution may be useful. We attempt to find efficient formulations of these tasks as applied to the extracting customer behavior information in a retail marketing context. Based on these formulations, we present a system that visually tracks customers in a retail store and performs a number of activity analysis tasks based on the output from the tracker. In tracking we introduce new techniques for pedestrian detection, initialization of the body model and a formulation of the temporal tracking as a global trans-dimensional optimization problem. Initial human detection is addressed by a novel method for head detection, which incorporates the knowledge of the camera projection model.The initialization of the human body model is addressed by newly developed shape and appearance descriptors. Temporal tracking of customer trajectories is performed by employing a human body tracking system designed as a Bayesian jump-diffusion filter. This approach demonstrates the ability to overcome model dimensionality ambiguities as people are leaving and entering the scene. Following the tracking, we developed a two-stage group activity formulation based upon the ideas from swarming research. For modeling purposes, all moving actors in the scene are viewed here as simplistic agents in the swarm. This allows to effectively define a set of inter-agent interactions, which combine to derive a distance metric used in further swarm clustering. This way, in the first stage the shoppers that belong to the same group are identified by deterministically clustering bodies to detect short term events and in the second stage events are post-processed to form clusters of group activities with fuzzy memberships. Quantitative analysis of the tracking subsystem shows an improvement over the state of the art methods, if used under similar conditions. Finally, based on the output from the tracker, the activity recognition procedure achieves over 80% correct shopper group detection, as validated by the human generated ground truth results

Review of traffic data collection methods for drivers’ car – following behaviour under various weather conditions

Adverse weather conditions have considerable impact on traffic operation and safety as it affects drivers’ car-following behaviour. However, the quality of traffic data and its related methodologies to address these effects are under continuous enhancement. This paper intends to provide an overview of various empirical traffic data collection methodologies widely used to investigate drivers car-following behaviour under various weather conditions. These methodologies include video cameras, pneumatic tubes, floating car data, instrumented vehicle and driving simulator. Moreover, the advantages and disadvantages related to methodologies have been discussed with emphasis on their suitability to work under adverse weather conditions. Furthermore, conclusion also comprises on table format of comparative review of facilities concerned with the methodologies

A multi-camera approach to image-based rendering and 3-D/Multiview display of ancient chinese artifacts

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QUANTUM ROAD TRAFFIC MODEL FOR AMBULANCE TRAVEL TIME ESTIMATION

Efficient management of ambulance utilisation is a vital issue for life saving. Knowledge of the amount of time needed for an ambulance to get to the hospital and when it will be available for a new task, can be estimated using modern Intelligent Transport Systems. Their main feature is an ability to simulate the state of traffic not only in long term, but also the real time events like accidents or high congestion, using microscopic models. The paper introduces usage of Quantum Computing paradigm to propose a quantum model of road traffic, which can track the state of traffic and estimate the travel time of vehicles. Model, if run on quantum computer can simulate the traffic in vast areas in real time. Proposed model was verified against the cellular automata model. Finally, application of quantum microscopic traffic models for ambulance vehicles was taken into consideration

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Adaptive background subtraction technique with unique feature representation for vehicle counting

Vehicle detection is the first step towards a successful traffic monitoring system. Although there were many studies for vehicle detection, only a few methods dealt with a complex situation especially in traffic jams. In addition, evaluation under different weather conditions (rainy, foggy and snowy) is so important for some countries but unfortunately it is rarely performed. Presently, vehicle detection is mainly performed using background subtraction method, yet it still faces many challenges. In this thesis, an adaptive background model based on the approximate median filter (AMF) is developed. To demonstrate its potential, the proposed method is further combined with two proposed feature representation techniques to be employed in either global or local vehicle detection strategy. In the global approach, an adaptive triangle-based threshold method is applied following the proposed adaptive background method. As a consequence, a better segmented foreground can be differentiated from the background regardless of the different weather conditions (i.e., rain, fog and snowfall). Comparisons with the adaptive local threshold (ALT) and the three frame differencing methods show that the proposed method achieves the average recall value of 85.94% and the average precision value of 79.53% with a negligible processing time difference. In the local approach, some predefined regions, instead of the whole image, will be used for the background subtraction operation. Subsequently, two feature representations, i.e. normalized object-area occupancy and normalized edge pixels are computed and formed into a feature vector, which is then fed into the k-means clustering technique. As illustrated in the results, the proposed method has shown an increment of at least 10% better in terms of the precision and 4.5% in terms of F1 score when compared to the existing methods. Once again, even with this significant improvement, the proposed method does not incur noticeable difference in the processing time. In conducting the experiments, different standard datasets have been used to show the performance of the proposed approach. In summary, the proposed method has shown better performances compared to three frame differencing and adaptive local threshold methods

Articulated human tracking and behavioural analysis in video sequences

Recently, there has been a dramatic growth of interest in the observation and tracking of human subjects through video sequences. Arguably, the principal impetus has come from the perceived demand for technological surveillance, however applications in entertainment, intelligent domiciles and medicine are also increasing. This thesis examines human articulated tracking and the classi cation of human movement, rst separately and then as a sequential process. First, this thesis considers the development and training of a 3D model of human body structure and dynamics. To process video sequences, an observation model is also designed with a multi-component likelihood based on edge, silhouette and colour. This is de ned on the articulated limbs, and visible from a single or multiple cameras, each of which may be calibrated from that sequence. Second, for behavioural analysis, we develop a methodology in which actions and activities are described by semantic labels generated from a Movement Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was developed for human tracking that allows multi-level parameter search consistent with the body structure. This tracker relies on the articulated motion prediction provided by the MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to generate a probabilistic activity description with action labels. The implemented algorithms for tracking and behavioural analysis are tested extensively and independently against ground truth on human tracking and surveillance datasets. Dynamic models are shown to predict and generate synthetic motion, while MCM recovers both periodic and non-periodic activities, de ned either on the whole body or at the limb level. Tracking results are comparable with the state of the art, however the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS

Multi-scale window specification over streaming trajectories

Enormous amounts of positional information are collected by monitoring applications in domains such as fleet management cargo transport wildlife protection etc. With the advent of modern location-based services processing such data mostly focuses on providing real-time response to a variety of user requests in continuous and scalable fashion. An important class of such queries concerns evolving trajectories that continuously trace the streaming locations of moving objects like GPS-equipped vehicles commodities with RFID\u27s people with smartphones etc. In this work we propose an advanced windowing operator that enables online incremental examination of recent motion paths at multiple resolutions for numerous point entities. When applied against incoming positions this window can abstract trajectories at coarser representations towards the past while retaining progressively finer features closer to the present. We explain the semantics of such multi-scale sliding windows through parameterized functions that reflect the sequential nature of trajectories and can effectively capture their spatiotemporal properties. Such window specification goes beyond its usual role for non-blocking processing of multiple concurrent queries. Actually it can offer concrete subsequences from each trajectory thus preserving continuity in time and contiguity in space along the respective segments. Further we suggest language extensions in order to express characteristic spatiotemporal queries using windows. Finally we discuss algorithms for nested maintenance of multi-scale windows and evaluate their efficiency against streaming positional data offering empirical evidence of their benefits to online trajectory processing