Control Design and Implementation of Autonomous 2-DOF Wireless Visual Object Tracking System

Karena skala implementasi deteksi visual yang besar sebagai alat sensor dan navigasi, pelacakan target menggunakan manipulasi gambar untuk sistem robot otonom menjadi sebuah objek studi yang menarik bagi banyak peneliti. Hal ini pun memunculkan berbagai upaya untuk mengembangkan sistem yang dapat mendeteksi dan melacak target bergerak dengan menggunakan pemrosesan gambar atau video dalam kondisi real time. Meskipun begitu, pelacakan objek visual dapat menjadi subjek dari kesalahan karena manipulasi gambar. Kesalahan ini dapat menimbulkan ketidakpastian pada kontrol sistem yang dapat menyebabkan ketidakstabilan, terutama bagi operasi jarak jauh. Oleh karena itu, filter yang efektif yang dapat mengatasi atau mengurangi kesalahan ini sangatlah diperlukan dalam mengembangkan sistem pelacakan objek visual. Dalam karya ini, sebuah sistem pelacakan objek visual dalam 2 derajat kebebasan (2-DOF) dikembangkan dengan information filter atau filter informasi. Sistem ini terdiri dari sebuah unit pengambilan gambar, unit pengolahan gambar, komunikasi nirkabel, dan manipulator. Kemudian untuk mengamati efektivitas filter dalam kondisi real time dan jarak jauh, prestasi sistem pelacakan visual ini, baik dengan maupun tanpa filter tersebut, diuji berdasarkan simulasi video dan tes secara real time. Berdasarkan pengujian secara real time, filter informasi dapat mengurangi kesalahan pengukuran / deteksi sekitar 30% dibandingkan dengan deteksi tanpa menggunakan filter

Control Design and Implementation of Autonomous 2-DOF Wireless Visual Object Tracking System

Due to large scale implementation of visual detection and tracking as a mean of sensor and navigation tool, target detection and tracking using image manipulation for autonomous robotic system becomes an interesting object of study for many researchers. In addition, there have been attempts to develop a system that can detect and track a moving target by using an image or video processing in a real time condition. Despite that, visual object tracking can be a subject of noise because of image manipulation. The noise can create uncertainty on state and observation model that can lead to control instability, especially that in remote operation. Therefore, an effective filter that can tackle or reduce this noise is needed in developing a visual object tracking system. In this work, a 2-degree of freedom (2-DOF) visual object tracking system was developed with an information filter. The system consists of an image capture unit, an image processing unit, a wireless communication unit, and a manipulator. Then to observe the filter effectiveness on real time visual object tracking in remote operation, performances of this visual object tracking system with and without the filter were tested based on video simulation and real time tracking. In the live streaming test, the information filter can reduce the error of the measurement about 30% than that without it

Crime Monitoring and Controlling System by Mobile Device

The Closed Circuit Television (CCTV) have been used at very large scale for monitoring, recording and getting popular in whole world. The major goal of Closed Circuit Television system is monitoring or observing crime and tracking the objects. The smart phone Mobile world is also expanding at a rapid scale since the technology was invented. Most of smart phones users live in those countries where usage of CCTV system is very common in life. This project studies a monitoring system for smart phone mobile users based on CCTV system, where information will be sent from mobile phones to server so that CCTV system can work more specifically and accurately by monitoring and tracking objects. A safety assurance approach is proposed, in which a user can inform his location for close observation. If he/she feels like a potential threat. In that case of emergency situation, location, problem and all possible difficulties can be determined in comparatively less time by concern authorities like police as they have already monitoring the situation. DOI: 10.17762/ijritcc2321-8169.15012

Formalization of People and Crowd Detection and Tracking for Smart Video Surveillance

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the problem of detection and tracking of people and crowd in video. At first, we defined person, group of persons and crowd motion detection types and formalized them. For crowd, we defined three main types of its motion: direct motion, aggregation and dispersion. Then, we formalised the task of tracking for these three groups of people (single person, group of persons and crowd). Based on these formalizations, we developed algorithms for detection and tracking people and crowd in video sequences for indoor and outdoor environment. The results of experiments for video sequences obtained using a stationary and moving video camera are presented

Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.Comment: 8 pages, 7 figure

3D Flapping Trajectory of a Micro-Air-Vehicle and its Application to Unsteady Flow Simulation

[[abstract]]A three-dimensional (3D) trajectory detection framework using two high-speed cameras for the flapping flexible wing of a micro-air-vehicle (MAV) is presented. This MAV, which is called the “Golden Snitch”, has a successful flight record of 8 minutes. We embed the flexible wingskin with a nine light emitting diode (LED) array as the light enhancing marker and capsulate it with parylene (poly-para-xylylene) as the protection layer. We confirm an oblique figure of eight trajectory of this MAV’s wing with time-varying coordinate data. The corresponding aerofoil of the main wings’ profiles was subjected to the time-varying coordinate data, yielding a resolution of a 1/70 wing beating cycle of 15Hz flapping. The trajectory information is first demonstrated as the moving boundaries of an unsteady flow simulation around a flapping flexible wing.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[booktype]]紙本[[countrycodes]]HR

Real-Time Cleaning and Refinement of Facial Animation Signals

With the increasing demand for real-time animated 3D content in the entertainment industry and beyond, performance-based animation has garnered interest among both academic and industrial communities. While recent solutions for motion-capture animation have achieved impressive results, handmade post-processing is often needed, as the generated animations often contain artifacts. Existing real-time motion capture solutions have opted for standard signal processing methods to strengthen temporal coherence of the resulting animations and remove inaccuracies. While these methods produce smooth results, they inherently filter-out part of the dynamics of facial motion, such as high frequency transient movements. In this work, we propose a real-time animation refining system that preserves -- or even restores -- the natural dynamics of facial motions. To do so, we leverage an off-the-shelf recurrent neural network architecture that learns proper facial dynamics patterns on clean animation data. We parametrize our system using the temporal derivatives of the signal, enabling our network to process animations at any framerate. Qualitative results show that our system is able to retrieve natural motion signals from noisy or degraded input animation.Comment: ICGSP 2020: Proceedings of the 2020 The 4th International Conference on Graphics and Signal Processin

Person Re-identification in Videos by Analyzing Spatio-temporal Tubes

Typical person re-identification frameworks search for k best matches in a gallery of images that are often collected in varying conditions. The gallery usually contains image sequences for video re-identification applications. However, such a process is time consuming as video re-identification involves carrying out the matching process multiple times. In this paper, we propose a new method that extracts spatio-temporal frame sequences or tubes of moving persons and performs the re-identification in quick time. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization technique. Finally, a hierarchical re-identification framework is proposed and used to rank the output tubes. Experiments with publicly available video re-identification datasets reveal that our framework is better than existing methods. It ranks the tubes with an average increase in the CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Re-identification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community