A Soft-YoloV4 for High-Performance Head Detection and Counting

Zhen Zhang, Shihao Xia, Yuxing Cai, Cuimei Yang and Shaoning Zeng
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Zhen Zhang: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Shihao Xia: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Yuxing Cai: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Cuimei Yang: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Shaoning Zeng: Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Huzhou 313000, China

Mathematics, 2021, vol. 9, issue 23, 1-11

Abstract: Blockage of pedestrians will cause inaccurate people counting, and people’s heads are easily blocked by each other in crowded occasions. To reduce missed detections as much as possible and improve the capability of the detection model, this paper proposes a new people counting method, named Soft-YoloV4, by attenuating the score of adjacent detection frames to prevent the occurrence of missed detection. The proposed Soft-YoloV4 improves the accuracy of people counting and reduces the incorrect elimination of the detection frames when heads are blocked by each other. Compared with the state-of-the-art YoloV4, the AP value of the proposed head detection method is increased from 88.52 to 90.54%. The Soft-YoloV4 model has much higher robustness and a lower missed detection rate for head detection, and therefore it dramatically improves the accuracy of people counting.

Keywords: head detection; YoloV4; NMS; soft-NMS; people counting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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