Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vision-based Excavator Activity Analysis and Safety Monitoring System   In this paper, we propose an excavator activity analysis and safety
monitoring system, leveraging recent advancements in deep learning and computer
vision. Our proposed system detects the surrounding environment and the
excavators while estimating the poses and actions of the excavators. Compared
to previous systems, our method achieves higher accuracy in object detection,
pose estimation, and action recognition tasks. In addition, we build an
excavator dataset using the Autonomous Excavator System (AES) on the waste
disposal recycle scene to demonstrate the effectiveness of our system. We also
evaluate our method on a benchmark construction dataset. The experimental
results show that the proposed action recognition approach outperforms the
state-of-the-art approaches on top-1 accuracy by about 5.18%.

13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
2
The categories of this paper are: math