Tracking of Humans in Industrial Environments

Document Type:Essay

Subject Area:Computer Science

Document 1

Previous Application of Artificial Intelligence 4 4. Use of FPGAs 5 5. Use of Video Surveillance Systems 8 6. Analysis of Research Trends 9 6. Use of hardware acceleration for image processing algorithms (FPGAs vs. ASICs) 9 6. Use of Visual hull and Articulated ICP 10 6. Customized vision system 11 6. Heterogeneous computing 13 6. People Detection Process through Algorithms 14 7. YouTube Videos/Website Images 19 7. Video clip 1 19 7. Video clip 2 19 Conclusion 20 References 21 1. Introduction In the modern industrial environments, according to Mosberger and Andreasson (2013), many position systems have been structured within such environments. Such systems possess a long-term stable macro-structure possessing possible small-scale dynamic structure. It is as a result of these assumptions where industries attempt to enable the previous positioning designs to offer and apply stabilized maps. Nevertheless, high levels of dynamic industrial contexts may result in challenges when tracking humans. The use of a variety of hardware acceleration helps in computer intelligence and the role it plays in dynamic industrial environments (Mosberger & Andreasson, 2013). This paper seeks to present a novel allocation structure for tracking individuals in industrial situations. It will show how hardware acceleration for computer intelligence can be employed in dynamic industrial environments. It will also illustrate the element of Field Programmable Gate Arrays (FPGAs) as part of hardware acceleration for image processing algorithms in industrial applications. The paper will also present the aspect of fast 3D reconstruction techniques for industrial applications including the profiling of people. It will also demonstrate a clear understanding of face recognition for lateral faces. Hardware acceleration for computer intelligence The use of hardware acceleration for computer intelligence would undertake a significant role in the tracking of humans in industrial environments.

Sign up to view the full document!

Hardware acceleration enhances artificial intelligence (AI) applications. As supported by Schwabe (2014), the application of Graphics Processing Units (GPUs) is vital for the manipulation of images. The mathematical perspective of neural networks is similar to the manipulation of pictures aimed at tracking humans. Even though GPUs are popular in how they work to enhance artificial intelligence, they continue evolving in a direction to facilitate deep learning and training needs. Previous Application of Artificial Intelligence Da Xu et al. illustrate that the application of artificial intelligence in the industrial conditions is used to track individuals through identifying risk and their levels of emotional responses. The ability to identify these signals in humans, aligned with artificial intelligence, helps to reduce accidents in the industrial settings. The use of automotive AI would also be used in the identification of humans who are working under the influence of alcohol, drugs, or related-substances (Munaro et al.

Sign up to view the full document!

In history, BMW partnered with Allianz Insurance Group to identify artificial intelligence-based items to enhance safety in its industry. Through the use of technology and leveraging on the firms’ artificial intelligence platforms, solutions were created to track the humans’ alertness levels and unsafe work habits. Use of FPGAs The FPGAs are used to track humans in various industrial control applications. The existing industrial networking protocols offer seamless, effective communication between the modules. This is relevant in that it allows elements from various manufacturers who are linked with the industry. The use of FPGAs in industries takes the form of three categories. The device category offers communication between the modules within short periods (Rodríguez-Andina et al. The process category enables peers within the industrial contexts to communicate with each other and allows a greater latency than the device category communication.

Sign up to view the full document!

However, modern-day intelligent cameras are used for capturing and analyzing data in real-time, compressing the collected data, and process it for use in analyzing tracking processes. Use of Video Surveillance Systems Nevertheless, the modern application of video surveillance systems in industrial systems has eradicated the use of traditional standards of tracking humans (Michel et al. In this case, the coax cables are used alongside the Cat-5 Ethernet cables. The effectiveness of the Coax is that it provides bandwidth to other elements which cannot support the high-level resolution in new IP sensors for tracking purpose (Gajjar et al. As a result, in this execution, FPGAs are employed as companion devices to existing systems. From a traditional perspective, FPGAs were majorly employed for signal processing and analysis of network packets (Nurvitadhi et al.

Sign up to view the full document!

However, as a result of the embedment of high-speed resources in the FPGA designs, the FPGAs are applied to trigger the algorithm as independent systems or coprocessors in human tracking. Apart from the reprogramming tasks, the utilization of the FPGA systems has various benefits which cannot be provided by the ASIC in tracking humans in the dynamic industrial systems (Andina et al. Use of Visual hull and Articulated ICP Michel et al. assert that visual hull and articulated ICP are applied in capturing markerless human motion. The used cost function encompasses two elements that ensure that suitable relevant points match. The application of different cameras to enhance the quality of the visual hulls and an improved knowledge helps industries to track human figures in different sites. Customized vision system Tracking industrial workers used observations on reflective patterns on their safety clothing is an appropriate mechanism.

Sign up to view the full document!

This also takes into perspective the element of color vision. In this context, color vision entails the application of different tracking algorithms for tracking and monitoring construction workers from both dynamic and static cameras (Aydalot & Keeble, 2018). Nevertheless, the operational contexts that industrial vehicles encounter a lot of challenges are based on various perspectives. This is because they engage in their activities differently in bright outdoor and inadequately illuminated indoor sections. This results to a wide range of varied light situations, a perspective which hinders the involvement of purely visible-light vision depending on detectors which use appropriate contrast and texture regarding the image (Aydalot & Keeble, 2018). In the outdoor uses, they are usually faced with the challenge of the rough terrain. When operating alongside thermal vision sensors, the availability of different heat sources in industry-based working site may make the attainment of background areas more challenging than in road traffic (Irwansyah et al.

Sign up to view the full document!

The monitoring range is evaluated by the volume and the region of cameras in a certain building (Sapiezynski et al. Through the use of heterogeneous computing designs, feature data collected from cameras are applied to detect a target. It is also utilized to keep tracking a particular target. This form of computing also puts into consideration the IP address of every gateway to keep tracking across heterogeneous designs (Haque et al. The gateway serve has a gateway list which lists the necessary gateway serve of other systems used to transmitting tracking request to other suitable systems. meters from the ground. Afterward, there is a need to eliminate high-level overlapping detection windows emanating from the consistency module. The activity enables to significantly decrease the number of detection windows that need to be evaluated through various phases of the detection cascade.

Sign up to view the full document!

The depth-related algorithms are used to identify few number of detection windows which can appropriately be evaluated using different phases of the cascade (Stewart et al. However, this is different from the consistency method in that it needs a point cloud rather than a disparity-based image. About the RGB-D human detection approaches, it is evident that there are favorable outcomes (Stahlschmidt et al. The safety approaches dictate that no individual can be missed in the industrial applications of tracking people. Hence, there is a need to undertake further envisioning and improvement of the process of detecting people. Also, the approaches used should focus on the need for ensuring that the tracking algorithms are more scalable regarding the volume of tracked individuals. Three dimensional (3D) image reconstruction is a modern process of formulating a mathematical representation of 3D objects.

Sign up to view the full document!

Through triangulation, the distinct contours of objects are vital in how they determine the images collected from the two unparalleled cameras used in the various sites in industrial settings. The other technique used in the 3D reconstruction systems is the photometric stereo. This technique has improved in recent times. It is a strategy in computer vision for determining the surface normal of items by observing the objects through various lighting situations. It is based on the assertion that the volume of light reflected by a surface is based on the surface’s orientation in association with the observer and the light sources (Liu et al. Improvements could be made to the photometric stereo through optimization of the reconstruction algorithm (Aggarwal & Xia, 2014). It helps to offer enhanced height estimates at sections with sharp edges in the industrial sites.

Sign up to view the full document!

The other modern approach which can be applied to track humans in industrial contexts includes the element of face segmentation for lateral faces. Face detection represents a crucial fundamental in enhancing safety and smooth operations in different industrial settings. The formulation of face segmentation triggers the capability to recognize faces in the visual working environment. com/watch?v=gRQpYLbQpRQ) 7. Video clip 2 The video begins by demonstrating how the industrial revolution used steam engines. It then illustrates the current fourth stage of the industrial revolution and how it influences both physical and biological systems. Hence, in the industrial environment, the element of the fourth industrial revolution is used to track people and promote their well-being in the industry (Antonimuthu, 2016). youtube. Xia, L. Human activity recognition from 3D Data: A review. Pattern Recognition Letters, 48, 70-80.

Sign up to view the full document!

Amendola, S. Bianchi, L. CRC Press. Antonimuthu, R. The Fourth Industrial Revolution. How Industry 4. is going to impact Human Life? Retrieved from https://www. Morgan Kaufmann. Cadavid, S. Selbie, W. S. D Dynamic Pose Estimation from Markerless Optical Data. Human Detection and Tracking for Video Surveillance: A Cognitive Science Approach. In ICCV Workshops (pp. Haque, A. Peng, B. Luo, Z. Hagemeyer, J. Porrmann, M. Rueckert, U. FPGA-based multi-robot tracking. Journal of Parallel and Distributed Computing, 107, 146-161. Panagiotakis, C. Argyros, A. A. Tracking the articulated motion of the human body with two RGBD cameras. Machine Vision and Applications, 26(1), 41-54. Lewis, C. Chambers, D. Hvass, P. Menegatti, E. RGB-D human detection and tracking for industrial environments. In Field-Programmable Technology (FPT), 2016 International Conference on (pp. IEEE. Prankl, J. Aldoma, A. Svejda, A. J. Advanced features and industrial applications of FPGAs—A review.

Sign up to view the full document!

IEEE Transactions on Industrial Informatics, 11(4), 853-864. Sapiezynski, P. Stopczynski, A. Velten, J. Kummert, A. Applications for a people detection and tracking algorithm using a time-of-flight camera. Multimedia Tools and Applications, 75(17), 10769-10786. Stewart, R. Pauly, M. August). Robust articulated‐ICP for real‐time hand tracking. In Computer Graphics Forum (Vol. No. Multimedia Tools and Applications, 74(3), 729-742. Yotsumoto, T. Nomura, A. Tanigawa, K. Takahashi, K.

Sign up to view the full document!

From $10 to earn access

Only on Studyloop

Original template

Downloadable