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Optimizing wireless video streams for computer vision

Hoek, F.J. van der (2019) Optimizing wireless video streams for computer vision.

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Abstract:The Dutch National police increasingly use robots for their operations, for example during observation and surveillance. The robots are equipped with a camera and transmit video data via a wireless video stream to the tele-operater, who uses the video for navigation. The teleoperator can be assisted, or replaced, by algorithms that use computer vision. However, the video data from the robots cannot be completely transmitted when the bit rate of wireless video streams is larger than the available throughput. This occurs, for example, when the wireless channel switches to a robust coding and modulation scheme, due to external disturbances. The incomplete data causes visible artefacts in the decoded video and computer vision algorithms cannot be effectively applied to such videos. The goal of this research is to determine how video streams can be optimized for computer vision, when the throughput is limited. The research is focussed on three types of video scaling that reduce data: spatial, temporal, and quality scaling. For these types of scaling, two questions are answered during the research: Can the required throughput of wireless video streams be reduced enough using spatial, temporal, and quality scaling, such that video data can be transferred completely? And how do spatial, temporal, and quality scaling affect computer vision? The impact of the three types of scaling on required throughput and computer vision, has been determined by analysing bit rate and visual tracking performance for videos generated from the RGB-D and CoRBS datasets, after applying different spatial, temporal, and quality scaling parameters. A custom visual tracking algorithm has been designed for the performance evaluation, based on direct visual simultaneous localization and mapping methods. It uses basic image processing techniques that are used inmost other computer vision algorithms, such that the results of the research are generalizable to such algorithms. The results indicate that combining the three types of scaling reduces the required throughput of a video enough, such that it is below the minimum available throughput of the IEEE 802.11 wifi standards. Of the three types, quality scaling did not impact tracking performance. Spatial scaling had a negative impact on tracking performance, but it also reduced the throughput. Temporal scaling had a bigger impact on tracking performance than spatial scaling, but a smaller impact on the required throughput. Based on the results, an optimal scaling strategy has been determined, that reduces throughput, while maximizing performance of computer vision algorithms. The optimal strategy is to first apply quality scaling on a video stream, until the lowest quality is reached, followed by spatial scaling, until the lowest resolution is reached, and finally temporal scaling to further reduce the required throughput. The results can be combined with related research to implement optimal wireless video streams on robots, such that computer vision algorithms can be effectively applied. Further research, on a larger number of videos, is required to determine the optimal scaling strategy for a specific throughput and to verify the optimal strategy in practice on a robot with a wireless video stream.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/80947
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