Real-time data acquisition, transmission and archival framework

Most human actions are a direct response to stimuli from their five senses. In the past few decades there has been a growing interest in capturing and storing the information that is obtained from the senses using analog and digital sensors. By storing this data it is possible to further analyze and better understand human perception. While many devices have been created for capturing and storing data, existing software and hardware architectures are aimed towards specialized devices and require expensive high-performance systems. This thesis aims to create a framework that supports capture and monitoring of a variety of sensors and can be scaled to run on low and high-performance systems such as netbooks, laptops and desktop systems. The proposed architecture was tested using aural and visual sensors due to their availability and higher bandwidth requirements compared to other sensors. Four different portable computing devices were used for testing with a varied set of hardware capabilities. On each of the systems the same suite of tests were run to benchmark and analyze CPU, memory, network, and storage usage statistics. From the results it was shown that on all of these platforms capturing data from multiple video, audio and other sensor sources was possible in real-time. Performance was shown to scale based on several factors, but the most important were CPU architecture, network topology and data interfaces used

MIJ2K Optimization using evolutionary multiobjective optimization algorithms

This paper deals with the multiobjective definition of video compression and its optimization. The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems. Video compression is defined as a problem including two competing objectives. We try to find a set of optimal, so-called Pareto-optimal solutions, instead of a single optimal solution. The two competing objectives are quality and compression ratio maximization. The optimization will be achieved using a new patent pending codec, called MIJ2K, also outlined in this paper. Video will be compressed with the MIJ2K codec applied to some classical videos used for performance measurement, selected from the Xiph.org Foundation repository. The result of the optimization will be a set of near-optimal encoder parameters. We also present the convergence of NSGA-II with different encoder parameters and discuss the suitability of MOEAs as opposed to classical search-based techniques in this field.This work was supported in part by Projects CICYT TIN2008- 06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.publicad

Video Sensor Architecture for Surveillance Applications

This paper introduces a flexible hardware and software architecture for a smart video sensor. This sensor has been applied in a video surveillance application where some of these video sensors are deployed, constituting the sensory nodes of a distributed surveillance system. In this system, a video sensor node processes images locally in order to extract objects of interest, and classify them. The sensor node reports the processing results to other nodes in the cloud (a user or higher level software) in the form of an XML description. The hardware architecture of each sensor node has been developed using two DSP processors and an FPGA that controls, in a flexible way, the interconnection among processors and the image data flow. The developed node software is based on pluggable components and runs on a provided execution run-time. Some basic and application-specific software components have been developed, in particular: acquisition, segmentation, labeling, tracking, classification and feature extraction. Preliminary results demonstrate that the system can achieve up to 7.5 frames per second in the worst case, and the true positive rates in the classification of objects are better than 80%. © 2012 by the authors; licensee MDPI, Basel, Switzerland.This work has been partially supported by SENSE project (Specific Targeted Research Project within the thematic priority IST 2.5.3 of the 6th Framework Program of the European Commission: IST Project 033279), and has been also co-funded by the Spanish research projects SIDIRELI: DPI2008-06737-C02-01/02 and COBAMI: DPI2011-28507-C02-02, both partially supported with European FEDER funds.Sánchez Peñarroja, J.; Benet Gilabert, G.; Simó Ten, JE. (2012). Video Sensor Architecture for Surveillance Applications. Sensors. 12(2):1509-1528. https://doi.org/10.3390/s120201509S15091528122Batlle, J. (2002). A New FPGA/DSP-Based Parallel Architecture for Real-Time Image Processing. Real-Time Imaging, 8(5), 345-356. doi:10.1006/rtim.2001.0273Foresti, G. L., Micheloni, C., Piciarelli, C., & Snidaro, L. (2009). Visual Sensor Technology for Advanced Surveillance Systems: Historical View, Technological Aspects and Research Activities in Italy. Sensors, 9(4), 2252-2270. doi:10.3390/s90402252Bramberger, M., Doblander, A., Maier, A., Rinner, B., & Schwabach, H. (2006). Distributed Embedded Smart Cameras for Surveillance Applications. Computer, 39(2), 68-75. doi:10.1109/mc.2006.55Foresti, G. L., Micheloni, C., Snidaro, L., Remagnino, P., & Ellis, T. (2005). Active video-based surveillance system: the low-level image and video processing techniques needed for implementation. IEEE Signal Processing Magazine, 22(2), 25-37. doi:10.1109/msp.2005.1406473Fuentes, L. M., & Velastin, S. A. (2003). Tracking People for Automatic Surveillance Applications. Lecture Notes in Computer Science, 238-245. doi:10.1007/978-3-540-44871-6_28García, J., Pérez, O., Berlanga, A., & Molina, J. M. (2007). Video tracking system optimization using evolution strategies. International Journal of Imaging Systems and Technology, 17(2), 75-90. doi:10.1002/ima.20100Xu, H., Lv, J., Chen, X., Gong, X., & Yang, C. (2007). Design of video processing and testing system based on DSP and FPGA. 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment. doi:10.1117/12.783790Sanfeliu, A., Andrade-Cetto, J., Barbosa, M., Bowden, R., Capitán, J., Corominas, A., … Spaan, M. T. J. (2010). Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas. Sensors, 10(3), 2274-2314. doi:10.3390/s100302274http://www.sense-ist.orgXu, H., Lv, J., Chen, X., Gong, X., & Yang, C. (2007). Design of video processing and testing system based on DSP and FPGA. 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment. doi:10.1117/12.78379

Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166

Side information exploitation, quality control and low complexity implementation for distributed video coding

Distributed video coding (DVC) is a new video coding methodology that shifts the highly complex motion search components from the encoder to the decoder, such a video coder would have a great advantage in encoding speed and it is still able to achieve similar rate-distortion performance as the conventional coding solutions. Applications include wireless video sensor networks, mobile video cameras and wireless video surveillance, etc. Although many progresses have been made in DVC over the past ten years, there is still a gap in RD performance between conventional video coding solutions and DVC. The latest development of DVC is still far from standardization and practical use. The key problems remain in the areas such as accurate and efficient side information generation and refinement, quality control between Wyner-Ziv frames and key frames, correlation noise modelling and decoder complexity, etc. Under this context, this thesis proposes solutions to improve the state-of-the-art side information refinement schemes, enable consistent quality control over decoded frames during coding process and implement highly efficient DVC codec. This thesis investigates the impact of reference frames on side information generation and reveals that reference frames have the potential to be better side information than the extensively used interpolated frames. Based on this investigation, we also propose a motion range prediction (MRP) method to exploit reference frames and precisely guide the statistical motion learning process. Extensive simulation results show that choosing reference frames as SI performs competitively, and sometimes even better than interpolated frames. Furthermore, the proposed MRP method is shown to significantly reduce the decoding complexity without degrading any RD performance. To minimize the block artifacts and achieve consistent improvement in both subjective and objective quality of side information, we propose a novel side information synthesis framework working on pixel granularity. We synthesize the SI at pixel level to minimize the block artifacts and adaptively change the correlation noise model according to the new SI. Furthermore, we have fully implemented a state-of-the-art DVC decoder with the proposed framework using serial and parallel processing technologies to identify bottlenecks and areas to further reduce the decoding complexity, which is another major challenge for future practical DVC system deployments. The performance is evaluated based on the latest transform domain DVC codec and compared with different standard codecs. Extensive experimental results show substantial and consistent rate-distortion gains over standard video codecs and significant speedup over serial implementation. In order to bring the state-of-the-art DVC one step closer to practical use, we address the problem of distortion variation introduced by typical rate control algorithms, especially in a variable bit rate environment. Simulation results show that the proposed quality control algorithm is capable to meet user defined target distortion and maintain a rather small variation for sequence with slow motion and performs similar to fixed quantization for fast motion sequence at the cost of some RD performance. Finally, we propose the first implementation of a distributed video encoder on a Texas Instruments TMS320DM6437 digital signal processor. The WZ encoder is efficiently implemented, using rate adaptive low-density-parity-check accumulative (LDPCA) codes, exploiting the hardware features and optimization techniques to improve the overall performance. Implementation results show that the WZ encoder is able to encode at 134M instruction cycles per QCIF frame on a TMS320DM6437 DSP running at 700MHz. This results in encoder speed 29 times faster than non-optimized encoder implementation. We also implemented a highly efficient DVC decoder using both serial and parallel technology based on a PC-HPC (high performance cluster) architecture, where the encoder is running in a general purpose PC and the decoder is running in a multicore HPC. The experimental results show that the parallelized decoder can achieve about 10 times speedup under various bit-rates and GOP sizes compared to the serial implementation and significant RD gains with regards to the state-of-the-art DISCOVER codec

Design and Implementation of HD Wireless Video Transmission System Based on Millimeter Wave

With the improvement of optical fiber communication network construction and the improvement of camera technology, the video that the terminal can receive becomes clearer, with resolution up to 4K. Although optical fiber communication has high bandwidth and fast transmission speed, it is not the best solution for indoor short-distance video transmission in terms of cost, laying difficulty and speed. In this context, this thesis proposes to design and implement a multi-channel wireless HD video transmission system with high transmission performance by using the 60GHz millimeter wave technology, aiming to improve the bandwidth from optical nodes to wireless terminals and improve the quality of video transmission. This thesis mainly covers the following parts: (1) This thesis implements wireless video transmission algorithm, which is divided into wireless transmission algorithm and video transmission algorithm, such as 64QAM modulation and demodulation algorithm, H.264 video algorithm and YUV420P algorithm. (2) This thesis designs the hardware of wireless HD video transmission system, including network processing unit (NPU) and millimeter wave module. Millimeter wave module uses RWM6050 baseband chip and TRX-BF01 rf chip. This thesis will design the corresponding hardware circuit based on the above chip, such as 10Gb/s network port, PCIE. (3) This thesis realizes the software design of wireless HD video transmission system, selects FFmpeg and Nginx to build the sending platform of video transmission system on NPU, and realizes video multiplex transmission with Docker. On the receiving platform of video transmission, FFmpeg and Qt are selected to realize video decoding, and OpenGL is combined to realize video playback. (4) Finally, the thesis completed the wireless HD video transmission system test, including pressure test, Web test and application scenario test. It has been verified that its HD video wireless transmission system can transmit HD VR video with three-channel bit rate of 1.2GB /s, and its rate can reach up to 3.7GB /s, which meets the research goal