Speeding Up Semantic Instance Segmentation by Using Motion Information

Otilia Zvorișteanu, Simona Caraiman and Vasile-Ion Manta
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Otilia Zvorișteanu: Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, D. Mangeron 27, 700050 Iasi, Romania
Simona Caraiman: Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, D. Mangeron 27, 700050 Iasi, Romania
Vasile-Ion Manta: Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, D. Mangeron 27, 700050 Iasi, Romania

Mathematics, 2022, vol. 10, issue 14, 1-19

Abstract: Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware resources to run or a longer execution time. Generally, the main efforts were to improve the accuracy of these methods rather than make them faster. This paper presents a novel solution to speed up the semantic instance segmentation task. The solution combines two state-of-the-art methods from semantic instance segmentation and optical flow fields. To reduce the inference time, the proposed framework (i) runs the inference on every 5th frame, and (ii) for the remaining four frames, it uses the motion map computed by optical flow to warp the instance segmentation output. Using this strategy, the execution time is strongly reduced while preserving the accuracy at state-of-the-art levels. We evaluate our solution on two datasets using available benchmarks. Then, we conclude on the results obtained, highlighting the accuracy of the solution and the real-time operation capability.

Keywords: machine vision; scene understanding; semantic instance segmentation; dense optical flow; real-time (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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