Three-dimensional object tracking system and method employing plural sensors and plural processors for performing parallel processing

A multiple sensor/multiple processor 3-D tracking system includes multiple sensor (camera) and DSP combinations and a controller connected so as to create a parallel computer from the multiple DSPs. The camera/DSP combinations and the controller are connected together to form a ring. For 3-D position measurement and tracking applications, each camera/DSP combination is programmed with an identical program to function as a node in a true parallel computer, and the transformation equations are solved in a distributed fashion using the entire parallel machine. The distributed processor processes the camera x and y point measurements in a distributed and parallel fashion to yield a solution to the coordinate transformation equations much more quickly than is possible with multiple camera/DSP combinations and a single processor

Enabling Runtime Self-Coordination of Reconfigurable Embedded Smart Cameras in Distributed Networks

Smart camera networks are real-time distributed embedded systems able to perform computer vision using multiple cameras. This new approach is a confluence of four major disciplines (computer vision, image sensors, embedded computing and sensor networks) and has been subject of intensive work in the past decades. The recent advances in computer vision and network communication, and the rapid growing in the field of high-performance computing, especially using reconfigurable devices, have enabled the design of more robust smart camera systems. Despite these advancements, the effectiveness of current networked vision systems (compared to their operating costs) is still disappointing; the main reason being the poor coordination among cameras entities at runtime and the lack of a clear formalism to dynamically capture and address the self-organization problem without relying on human intervention. In this dissertation, we investigate the use of a declarative-based modeling approach for capturing runtime self-coordination. We combine modeling approaches borrowed from logic programming, computer vision techniques, and high-performance computing for the design of an autonomous and cooperative smart camera. We propose a compact modeling approach based on Answer Set Programming for architecture synthesis of a system-on-reconfigurable-chip camera that is able to support the runtime cooperative work and collaboration with other camera nodes in a distributed network setup. Additionally, we propose a declarative approach for modeling runtime camera self-coordination for distributed object tracking in which moving targets are handed over in a distributed manner and recovered in case of node failure

The Fly's Eye Camera System -- an instrument design for large \'etendue time-domain survey

In this paper we briefly summarize the design concepts of the Fly's Eye Camera System, a proposed high resolution all-sky monitoring device which intends to perform high cadence time domain astronomy in multiple optical passbands while still accomplish a high \'etendue. Fundings have already been accepted by the Hungarian Academy of Sciences in order to design and build a Fly's Eye device unit. Beyond the technical details and the actual scientific goals, this paper also discusses the possibilities and yields of a network operation involving ~10 sites distributed geographically in a nearly homogeneous manner. Currently, we expect to finalize the mount assembly -- that performs the sidereal tracking during the exposures -- until the end of 2012 and to have a working prototype with a reduced number of individual cameras sometimes in the spring or summer of 2013.Comment: Accepted for publication in AN, 4.05 pages. Website of the project: http://flyseye.net

On the design and implementation of a high definition multi-view intelligent video surveillance system

This paper proposes a distributed architecture for high definition (HD) multi-view video surveillance system. It adopts a modular design where multiple intelligent Internet Protocol (IP)-based video surveillance cameras are connected to a local video server. Each server is equipped with storage and optional graphics processing units (GPUs) for supporting high-level video analytics and processing algorithms such as real-time decoding and tracking for the video captured. The servers are connected to the IP network for supporting distributed processing and remote data access. The DSP-based surveillance camera is equipped with realtime algorithms for streaming compressed videos to the server and performing simple video analytics functions. We also developed video analytics algorithms for security monitoring. Both publicly available data set and real video data that are captured under indoor and outdoor scenarios are used to validate our algorithms. Experimental results show that our distributed system can support real-time video applications with high definition resolution.published_or_final_versio

Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame

Passive monitoring of large sites typically requires coordination between multiple cameras, which in turn requires methods for automatically relating events between distributed cameras. This paper tackles the problem of self-calibration of multiple cameras which are very far apart, using feature correspondences to determine the camera geometry. The key problem is finding such correspondences. Since the camera geometry and photometric characteristics vary considerably between images, one cannot use brightness and/or proximity constraints. Instead we apply planar geometric constraints to moving objects in the scene in order to align the scene"s ground plane across multiple views. We do not assume synchronized cameras, and we show that enforcing geometric constraints enables us to align the tracking data in time. Once we have recovered the homography which aligns the planar structure in the scene, we can compute from the homography matrix the 3D position of the plane and the relative camera positions. This in turn enables us to recover a homography matrix which maps the images to an overhead view. We demonstrate this technique in two settings: a controlled lab setting where we test the effects of errors in internal camera calibration, and an uncontrolled, outdoor setting in which the full procedure is applied to external camera calibration and ground plane recovery. In spite of noise in the internal camera parameters and image data, the system successfully recovers both planar structure and relative camera positions in both settings

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

Distributed Monocular SLAM for Indoor Map Building

Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment

MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications

According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente

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