Scalable Video Coding Based on Wireless Sensor

Scalable Video Coding Based on Wireless Sensor Networks for Monitoring Object K. Rantelobo1,a) 1

Electrical Engineering Department, Universitas Nusa Cendana, Kupang, NTT, Indonesia a)

Corresponding author: [email protected]

Abstract: The Scalable Video Coding (SVC) based on the Wireless Video Sensor Network (WVSN) as part of the camera's device and video transmission. This paper reviews how far SVC is used on WVSN devices and systems primarily in monitoring an object. This paper also proposes an object monitoring scheme using SVC on adaptive WVSN. Monitoring video-based purposes can use in a variety of applications and fields.

1. INTRODUCTION The technology development of Wireless Sensor Networks (WSN) has experienced a very rapid increase [1], [2]. Initially only processing scalar data, now it has been aimed at processing multimedia data. The use of multimedia-based sensors and the rapid development of signal processing have penetrated and changed the role of WSN in various fields. The method and application of video coding on video devices and systems at WSN have given birth to a new technology called Wireless Video Sensor Networks (WVSN) which will be the main discussion in this article [3] - [5]. In this article, we investigate the use of Scalable Video Coding methods, specifically Combined Scalable Video Coding (CSVC) on WVSN applications and devices. Application of CSVC on WVSN by utilizing Internet of Things (IoT) technology. The proposed scheme will be connecting to the internet network where data processing will be transmitted and stored in cloud facilities to facilitate development and operations. This application is expected to use in various fields that require monitoring object activities. This investigation is a series of development studies from various initial studies that the authors have done [6] [9]. The main focus is on video transmission on WVSN using the SVC method. Analysis of data retrieval and processing uses the process of tracking moving objects and is used in monitoring applications. This system will be transmitted to the internet with IoT technology. The main contribution of this paper is the use of the CSVC scheme on WVSN to monitor an object that is moving. The Region of Interest (ROI) method in the system and the connection to the internet using IoT technology is a new thing in monitoring object activities. To the best of our knowledge, this scheme has never been studying well, so we hope that the results will contribute to monitoring object activities in various fields of monitoring.

Wireless Video Sensor Networks (WVSN) The technology of Wireless Multimedia Sensor Network (JSMN) or Wireless Wireless Multimedia Sensor (WMSN) uses multimedia content as input. The video bitstream generated from the information will be transmitted with JSMN so that the user will receive it as a video file that will be processing according to the user's goals and desires. The use of JSMN applications is extensive, including multimedia surveillance sensor networks; documentation on various vital activities; monitoring traffic traffic; control and supervision system in industry;

biomedical systems and telemedicine; automation systems, security and monitoring of homes and residences; environmental supervision; personal care system and other private security services; and various industrial processes and services [1], [3], [4], [10]. JSMN Technology is the development of Wireless Sensor Network (JSN) technology or Wireless Sensor Network (WSN). The application of JSN to monitoring objects is video-based monitoring that is connecting to the internet network. The underlying architecture and configuration of a JSMN system depicted in Figure 1.

Figure 1. The WSN system in general The constituent component of a WVSN device consists of sensor devices, mainboards, communication devices, and data processing devices. Physically the WVSN device can be seen in Figure 2.

(a)

(b) Figure 2. The WVSN components

This study aims to develop WVSN using the latest video coding standards as state of the art namely H.265 or HEVC. WVSN development on adaptive communication systems that will produce new schemes for video transmission using wireless channels. It will be proposed utilizing other video processing methods, namely Region of Interest (ROI), tracking and the video detection [11] - [14], while the function structure chart of a WVSN system can depict in Figure 3.

Figure 3. The basic scheme of WSN Systems [2] The general classification and utilization of WSN as view in Figure 4. It seems that the monitoring and tracking functions are separate, but in some cases, they can be combining. In the topic of this study, monitoring objects, primarily the movers will be handled by tracking methods but in a monitoring scheme using the latest technology in sensor development, using Raspberry Pi 3 devices [15].

Figure 4. The classification of WSN in general [2]

Scalable Video Coding (SVC) The scalability method is proposed first to reduce the loss of packets (cell) in the ATM (Asynchronous Transfer Mode) network [16], where two classes of bit streams or layers have produced, namely base-layer and enhancementlayer. The layer that contains vital information is the base layer while the enhancements include the residual data in the process of improving the image quality or video that will produce, as shown in Figure 5.

Figure 5. The SVC Systems [6] Scalable Video Coding (SVC) is part of the development of the 10 part H.264 / MPEG-4 standard AVC (Advanced Video Coding) or H.264/AVC. The development process has taken around 20 years, starting from H.261 and MPEG-2, H.262 followed by H.263 + and MPEG-4. Since January 2005, MPEG and VCEG have joined the JVT project as an attempt to amend the H.264 / AVC process as an official standard [17]. Until now, the SVC standard is still in the process of amendment, a collaborative work of various parties in realizing the High-Efficiency Video Coding (HEVC) or H.265 standard, exceptionally Scalable HEVC (SHVC), which promises bitrate efficiency to 50% compared to H.264 [18], [19].

2. RELATED WORKS The WSN technology development starts with the use of sensors connected to data communication networks. [1]. The development of multimedia communication, the need for increased multimedia data has included in the WSN. The WVSN system then became of the latest product of WSN. Also known as Wireless Multimedia Sensor Networks (WMSN) [3], [4], [10]. The use of video coding in WVSN is also developing according to the latest coding. The latest video encoding, H.265 or better known as High-Efficiency Video Coding (HEVC), has begun to be implemented [20], where the use of SVC has already been applied [11]. There are various studies that have begun implementing HEVC coding on WVSN, including [4], [11], [12], [20] - [32]. The use of SVC on WVSN is still minimal due to software and hardware constraints. HEVC is become using for security in the smart city [33]; efficient energy use in wakeup/sleep conditions is shown by [34]. Image segmentation or object on a frame from a real-time video reviewed by Zhao [35]. The segmentation using for tracking and monitoring objects in the live event.

3. OVERVIEW OF THE SCHEME OF PROPOSED AND PRIMARILY RESULTS The scheme proposed in this paper is as shown in Figure 6. The project we have tested in various previous activities [36]. As for the scheme we developed in this paper using the H.265 / HEVC video coding standard. Tracking method to monitor an object using the Region of Interest (ROI) method.

Figure 6. Scheme of the system The parameters and components used in this work are as in Table 1. Table 1. Parameters and components are using in this works Spesifikasi/Keterangan Nama Komponen/Perangkat Raspberry Pi Version 3 (multimedia) Power Supply Solar cell Sensor Accelerator, Camera HD Type of Networks Wi-fi Data / editor software (application) OpenCV [37] The scheme that we will test and propose based on the testing scheme in Figure 6 is the development of a research scheme on monitoring applications in Agroindustry Plantations using a multi-sector approach such as Tourism, Animal Husbandry, and Agribusiness as shown in Figure 7. The main results of this research scheme only arrived at monitoring moving objects that had a simple movement, namely the flow in and out of a room. We have published the results [36], and the results we will release will be in the form of journals based on the scheme of this paper as shown in Figure 8. The test results from the development scheme are expected to apply in various fields for monitoring objects and even surveillance activities.

Figure 7. Multi-disciplinary development scheme in various fields of agro-industry

Figure 8. Example of image analysis results from monitoring object move (people calculation) where parts (a) to (d) are segmentation of images morphologically transformation, and section (f) is the actual image of objects (people entering and exiting a room)

4. CONCLUSION AND FUTURE WORK The conclusion that we can give is: that the video-based data usage scheme on WVSN can apply to monitor object activities. The monitoring object activity is carried out by tracking the object image captured from the camera on the WVSN device. For future works, utilizing video coding methods with the latest technology is expected to provide optimal results and contributions of this research. This system will combine with an IoT-based internet network that will provide flexibility in managing and monitoring various applications in the future.

ACKNOWLEDGMENTS The research is supported through Direktorat Research dan Pengabdian Masyarakat Dirjen Penguatan Research dan Pengembangan Kemenristekdikti Grants (Hibah Penelitian Berbasis Kompetensi) by Indonesian Ministry of Research and Higher Education (under Grant No. 91/UN15.19/LT/2018). We are very grateful to Mr. Meksianis Ndii for discussion and support; Lab. Teknik Elektro Universitas Nusa Cendana and Lab. Teknik Multimedia Universitas Udayana.

REFERENCES [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, pp. 393–422, Mar. 2002. [2] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Computer Networks, vol. 52, no. 12, pp. 2292–2330, Aug. 2008. [3] I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, “A survey on wireless multimedia sensor networks,” Computer Networks, vol. 51, no. 4, pp. 921–960, 2007. [4] E. Gürses and Ö. B. Akan, “Multimedia communication in wireless sensor networks,” Annales des Telecommunications/Annals of Telecommunications, vol. 60, no. 7–8, pp. 872–900, 2005. [5] B. Tavli, K. Bicakci, R. Zilan, and J. M. Barcelo-Ordinas, “A survey of visual sensor network platforms,” Multimedia Tools and Applications, vol. 60, no. 3, pp. 689–726, 2012. [6] K. Rantelobo, H. Lami, and Wirawan, “Video transmission using combined scalability video coding over MIMO-OFDM systems,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 4, no. 2, pp. 390–396, 2016. [7] N. P. Sastra, Wirawan, and G. Hendrantoro, “Virtual view image over wireless visual sensor network,” Telkomnika, vol. 9, no. 3, pp. 483–488, 2011. [8] D. M. Wiharta, Wirawan, and G. Hendrantoro, “On the Accuracy of Particle Filter-Based Object Tracking,” International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 11, pp. 265–276. [9] H. Z. Kotta, K. Rantelobo, S. Tena, and G. Klau, “Wireless sensor network for landslide monitoring in Nusa Tenggara Timur,” ℡KOMNIKA (Telecommunication Computing Electronics and Control), vol. 9, no. 1, pp. 9– 18, 2011. [10] S. Misra, M. Reisslein, and G. Xue, “A survey of multimedia streaming in wireless sensor networks,” IEEE Communications Surveys and Tutorials, vol. 10, no. 4, pp. 18–39, 2008. [11] M. Usman, N. Yang, M. A. Jan, X. He, M. Xu, and K.-M. Lam, “A Joint Framework for QoS and QoE for Video Transmission over Wireless Multimedia Sensor Networks,” IEEE Transactions on Mobile Computing, vol. 17, no. 4, pp. 746–759, 2018. [12] M. Wien, R. Cazoulat, A. Graffunder, A. Hutter, and P. Amon, “Real-Time System for Adaptive Video Streaming Based on SVC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 9, pp. 1227–1237, Sep. 2007. [13] A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Computing Surveys, vol. 38, no. 4, 2006. [14] M. Naderan, M. Dehghan, H. Pedram, and V. Hakami, “Survey of mobile object tracking protocols in wireless sensor networks: A network-centric perspective,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 11, no. 1, pp. 34–63, 2012. [15] R. P. Foundation, “Raspberry Pi — Teach, Learn, and Make with Raspberry Pi,” Raspberry Pi. [Online]. Available: https://www.raspberrypi.org.

[16] M. Ghanbari, “Two-layer coding of video signals for VBR networks,” IEEE Journal on Selected Areas in Communications, vol. 7, no. 5, pp. 771–781, Jun. 1989. [17] T. Wiegand and G. J. Sullivan, “The H. 264/AVC video coding standard [Standards in a Nutshell],” Signal Processing Magazine, IEEE, vol. 24, no. 2, pp. 148–153, 2007. [18] G. J. Han, J. R. Ohm, W.-J. Han, W.-J. Han, and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) Standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. PP, no. 99, p. 1, 2012. [19] H. Schwarz and M. Wien, “The scalable video coding extension of the H. 264/AVC standard,” IEEE Signal Processing Magazine, vol. 25, no. 2, p. 135, 2008. [20] L. Zhao, Z. He, W. Cao, and D. Zhao, “Real-Time Moving Object Segmentation and Classification from HEVC Compressed Surveillance Video,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1346–1357, 2018. [21] R. N. Handcock et al., “Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing,” Sensors, vol. 9, no. 5, pp. 3586–3603, 2009. [22] J. Horneber and A. Hergenroder, “A survey on testbeds and experimentation environments for wireless sensor networks,” IEEE Communications Surveys and Tutorials, vol. 16, no. 4, pp. 1820–1838, 2014. [23] W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 34, no. 3, pp. 334–352, 2004. [24] N. Imran, B.-C. Seet, and A. C. M. Fong, “Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures,” SpringerPlus, vol. 4, no. 1, 2015. [25] S. Kruthi and J. Kamalakannan, “Implementation of moving object detection and categorization from hevc compressed surveillance video,” International Journal of Mechanical Engineering and Technology, vol. 9, no. 4, pp. 35–42, 2018. [26] T. Kwon, Y. Choi, C. Bisdikian, and M. Naghshineh, “QoS provisioning in wireless/mobile multimedia networks using an adaptive framework,” Wireless Networks, vol. 9, no. 1, pp. 51–59, 2003. [27] M. Maimour, “SenseVid: A traffic trace based tool for QoE Video transmission assessment dedicated to Wireless Video Sensor Networks,” Simulation Modelling Practice and Theory, vol. 87, pp. 120–137, 2018. [28] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, “Wireless sensor networks for habitat monitoring,” presented at the Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications, 2002, pp. 88–97. [29] C. Nastasi and A. Cavallaro, “WiSE-MNet: an experimental environment for wireless multimedia sensor networks,” in Sensor Signal Processing for Defence (SSPD 2011), 2011, pp. 1–5. [30] B. T. Nguyen, L. Murphy, and G.-M. Muntean, “Enhanced scheme for adaptive multimedia delivery over wireless video sensor networks,” presented at the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, 2017. [31] F. Wu et al., “SMART: an efficient, scalable, and robust streaming video system,” EURASIP Journal on Advances in Signal Processing, vol. 2004, no. 2, pp. 192–206, 2004. [32] K. Sohn, Y. Y. Kim, and N. Saxena, “A new cross-layer qos-provisioning architecture in wireless multimedia sensor networks,” KSII Transactions on Internet and Information Systems, vol. 10, no. 12, pp. 5286–5306, 2016. [33] V. A. Memos, K. E. Psannis, Y. Ishibashi, B.-G. Kim, and B. B. Gupta, “An Efficient Algorithm for Mediabased Surveillance System (EAMSuS) in IoT Smart City Framework,” Future Generation Computer Systems, vol. 83, pp. 619–628, Jun. 2018. [34] B. N. Trinh, L. Murphy, and G. Muntean, “Uplink Adaptive Multimedia Delivery (UAMD) scheme for Video Sensor Network,” in 2017 IEEE International Conference on Communications Workshops (ICC Workshops), 2017, pp. 85–90. [35] L. Zhao, Z. He, W. Cao, and D. Zhao, “Real-Time Moving Object Segmentation and Classification From HEVC Compressed Surveillance Video,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1346–1357, Jun. 2018. [36] Rantelobo, K, Indraswara, M. A, Sastra, N. P, Wiharta, D. M, Lami. H. F. J, and Kotta, H. Z, “Monitoring Systems for Counting People using Raspberry Pi 3,” in ICSGTEIS 2018, Denpasar, Bali, 2018, p. Accepted. [37] “OpenCV library.” [Online]. Available: https://opencv.org/.