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Sensors 2019, 19(3), 729; https://doi.org/10.3390/s19030729

A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements

Brain-inspired Application Technology Center, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Received: 29 December 2018 / Revised: 30 January 2019 / Accepted: 8 February 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Abstract

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc. View Full-Text
Keywords: TOF; human detection; flow estimation; computational efficiency TOF; human detection; flow estimation; computational efficiency
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Wang, W.; Liu, P.; Ying, R.; Wang, J.; Qian, J.; Jia, J.; Gao, J. A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements. Sensors 2019, 19, 729.

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