Motor Focus: Ego-Motion Prediction with All-Pixel Matching

• URL: http://arxiv.org/abs/2404.17031v1
• Date: Thu, 25 Apr 2024 20:45:39 GMT
• Title: Motor Focus: Ego-Motion Prediction with All-Pixel Matching
• Authors: Hao Wang, Jiayou Qin, Xiwen Chen, Ashish Bastola, John Suchanek, Zihao Gong, Abolfazl Razi,
• Abstract summary: We introduce an image-only method that applies motion analysis using optical flow with ego-motion compensation to predict Motor Focus. This paper addresses the camera shaking issue in handheld and body-mounted devices which can severely degrade performance and accuracy. This also provides a robust, real-time solution that adapts to the user's immediate environment.
• Score: 3.837186701755568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motion analysis plays a critical role in various applications, from virtual reality and augmented reality to assistive visual navigation. Traditional self-driving technologies, while advanced, typically do not translate directly to pedestrian applications due to their reliance on extensive sensor arrays and non-feasible computational frameworks. This highlights a significant gap in applying these solutions to human users since human navigation introduces unique challenges, including the unpredictable nature of human movement, limited processing capabilities of portable devices, and the need for directional responsiveness due to the limited perception range of humans. In this project, we introduce an image-only method that applies motion analysis using optical flow with ego-motion compensation to predict Motor Focus-where and how humans or machines focus their movement intentions. Meanwhile, this paper addresses the camera shaking issue in handheld and body-mounted devices which can severely degrade performance and accuracy, by applying a Gaussian aggregation to stabilize the predicted motor focus area and enhance the prediction accuracy of movement direction. This also provides a robust, real-time solution that adapts to the user's immediate environment. Furthermore, in the experiments part, we show the qualitative analysis of motor focus estimation between the conventional dense optical flow-based method and the proposed method. In quantitative tests, we show the performance of the proposed method on a collected small dataset that is specialized for motor focus estimation tasks.

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