Tracking System with Re-identification Using a RGB String Kernel

International audiencePeople re-identification consists to identify a person which comes back in a scene where it has been previously detected. This key problem in visual surveillance applications may concern single or multi camera systems. Features encoding each person should be rich enough to provide an efficient re-identification while being sufficiently robust to remain significant through the different phenomena which may alter the appearance of a person in a video. We propose in this paper a method which encodes people's appearance through a string of salient points. The similarity between two such strings is encoded by a kernel. This last kernel is combined with a tracking algorithm in order to associate a set of strings to each person and to measure similarities between persons entering into the scene and persons who left it

Army-NASA aircrew/aircraft integration program (A3I) software detailed design document, phase 3

The capabilities and design approach of the MIDAS (Man-machine Integration Design and Analysis System) computer-aided engineering (CAE) workstation under development by the Army-NASA Aircrew/Aircraft Integration Program is detailed. This workstation uses graphic, symbolic, and numeric prototyping tools and human performance models as part of an integrated design/analysis environment for crewstation human engineering. Developed incrementally, the requirements and design for Phase 3 (Dec. 1987 to Jun. 1989) are described. Software tools/models developed or significantly modified during this phase included: an interactive 3-D graphic cockpit design editor; multiple-perspective graphic views to observe simulation scenarios; symbolic methods to model the mission decomposition, equipment functions, pilot tasking and loading, as well as control the simulation; a 3-D dynamic anthropometric model; an intermachine communications package; and a training assessment component. These components were successfully used during Phase 3 to demonstrate the complex interactions and human engineering findings involved with a proposed cockpit communications design change in a simulated AH-64A Apache helicopter/mission that maps to empirical data from a similar study and AH-1 Cobra flight test

A Lens-Calibrated Active Marker Metrology System

This paper presents a prototypical marker tracking system, MT, which is capable of recording multiple mobile robot trajectories in parallel for offline analysis. The system is also capable of providing trajectory data in realtime to agents (such as robots in an arena) and implements several multi-agent operators to simplify agent-based perception. The latter characteristic provides an ability to minimise the normally expensive process of implementing agent-centric perceptual mechanisms and provides a means for multiagent "global knowledge" (Parker 1993)

Low Cost Open Source Modal Virtual Environment Interfaces Using Full Body Motion Tracking and Hand Gesture Recognition

Virtual environments provide insightful and meaningful ways to explore data sets through immersive experiences. One of the ways immersion is achieved is through natural interaction methods instead of only a keyboard and mouse. Intuitive tracking systems for natural interfaces suitable for such environments are often expensive. Recently however, devices such as gesture tracking gloves and skeletal tracking systems have emerged in the consumer market. This project integrates gestural interfaces into an open source virtual reality toolkit using consumer grade input devices and generates a set of tools to enable multimodal gestural interface creation. The AnthroTronix AcceleGlove is used to augment body tracking data from a Microsoft Kinect with fine grained hand gesture data. The tools are found to be useful as a sample gestural interface is implemented using them. The project concludes by suggesting studies targeting gestural interfaces using such devices as well as other areas for further research

Low Cost Open Source Modal Virtual Environment Interfaces Using Full Body Motion Tracking and Hand Gesture Recognition

Virtual environments provide insightful and meaningful ways to explore data sets through immersive experiences. One of the ways immersion is achieved is through natural interaction methods instead of only a keyboard and mouse. Intuitive tracking systems for natural interfaces suitable for such environments are often expensive. Recently however, devices such as gesture tracking gloves and skeletal tracking systems have emerged in the consumer market. This project integrates gestural interfaces into an open source virtual reality toolkit using consumer grade input devices and generates a set of tools to enable multimodal gestural interface creation. The AnthroTronix AcceleGlove is used to augment body tracking data from a Microsoft Kinect with fine grained hand gesture data. The tools are found to be useful as a sample gestural interface is implemented using them. The project concludes by suggesting studies targeting gestural interfaces using such devices as well as other areas for further research

Scaling digital screen reading with one-shot learning and re-identification

Using only a mobile phone app, our objective is to cheaply retro-fit digital meters (e.g blood pressure, blood glucose or industrial gauges) with 'smart' data transfer capabilities. Using the mobile phone camera we build an app to securely and accurately transcribe information from digital meter screens. Only a single labelled training image of a target meter is required to build a custom screen reading module. Here we show how this can scale to potentially hundreds of different meters by learning to recognising the meter type so that the reading module can be automatically selected. This makes the system very easy for a user who would need to scan multiple different meter types. To this end, we build a CNN based system which runs in real-time on mobile device with very high read accuracy and meter recognition. Our contributions include (i) a method of one-shot training by synthesis through domain shift reduction, (ii) a deep embedding network for scale, translation and rotation invariant re-identification of digital meters, (iii) a highly accurate and efficient mobile phone app for recognising and parsing digital meter screens and (iv) release of a new digital meter re-identification dataset

畳み込みニューラルネットワークと時空間サポートベクター回帰に基づくギターの運指の自動評価システム

早大学位記番号:新7965早稲田大

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection