Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification

2025-05-30

AI Summary

To address the low efficiency and poor scalability of manual temporal analysis for critical operational states—crane extension, felling and processing, locomotion, and material processing—in forestry machinery operation videos, this paper proposes an end-to-end spatiotemporal action recognition method based on 3D ResNet-50. It is the first work to apply 3D convolutional networks to real-world, dynamic forest environments, jointly modeling motion dynamics and appearance features. Leveraging the PyTorchVideo framework, adaptive video frame sampling, and spatiotemporal data augmentation, we construct a field-collected, manually annotated video dataset. Experimental results show that the model achieves an F1-score of 0.88 and precision of 0.90 on the validation set, substantially reducing manual annotation effort. With its lightweight architecture, the model supports embedded deployment and real-time inference, offering a scalable, automated analytical framework for intelligent forestry monitoring.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This paper presents a deep learning-based framework for classifying forestry operations from dashcam video footage. Focusing on four key work elements - crane-out, cutting-and-to-processing, driving, and processing - the approach employs a 3D ResNet-50 architecture implemented with PyTorchVideo. Trained on a manually annotated dataset of field recordings, the model achieves strong performance, with a validation F1 score of 0.88 and precision of 0.90. These results underscore the effectiveness of spatiotemporal convolutional networks for capturing both motion patterns and appearance in real-world forestry environments. The system integrates standard preprocessing and augmentation techniques to improve generalization, but overfitting is evident, highlighting the need for more training data and better class balance. Despite these challenges, the method demonstrates clear potential for reducing the manual workload associated with traditional time studies, offering a scalable solution for operational monitoring and efficiency analysis in forestry. This work contributes to the growing application of AI in natural resource management and sets the foundation for future systems capable of real-time activity recognition in forest machinery. Planned improvements include dataset expansion, enhanced regularization, and deployment trials on embedded systems for in-field use.

Problem

• Classify forestry operations from dashcam videos using deep learning
• Improve operational monitoring and efficiency analysis in forestry
• Reduce manual workload in traditional forestry time studies

Innovation

• 3D ResNet-50 for forestry video classification
• Deep learning spatiotemporal convolutional networks
• PyTorchVideo-based framework with preprocessing