A Monkey Swing Counting Algorithm Based on Object Detection
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Abstract (arXiv)
This paper focuses on proposing a deep learning-based monkey swing counting algorithm. Nowadays, there are very few papers on monkey detection, and even fewer papers on monkey swing counting. This research focuses on this gap and attempts to count the number of monkeys swinging their heads by deep learning. This paper further extends the traditional target detection algorithm. By analyzing the results of object detection, we localize the monkey's actions over a period of time. This paper analyzes the task of counting monkey head swings, and proposes the standard that accurately describes a monkey swinging its head. Under the guidance of this standard, the head-swing count in 50 monkey movement videos in this paper has achieved 94%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 12 Mar 2023.
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8 of 16 references.
References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2018Markerless tracking of user-defined features with deep learningcited by 206
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