# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 1 Introduction
Nonetheless, traditional methods exhibit constrained accuracy in intricate scenarios as they necessitate meticulous feature design, which proves challenging in capturing intricate human behavior patterns. Deep learning has progressively emerged at the forefront of research in recent years [9], establishing itself as a predominant research tool in areas such as image recognition, image classification, and target detection [10]. It also presents novel opportunities for advancing human behavior recognition. Moreover, given the prevalent use of industrial communication devices, such as cameras, in agricultural settings, there exists an opportunity to integrate deep learning algorithms into these communication devices for the purpose of recognizing agricultural behaviors across diverse geographical regions. Scholars both at home and abroad have undertaken numerous endeavors to apply existing convolutional neural network algorithms to tasks related to video behavior recognition. Diverging from the scope of image classification, video behavior recognition necessitates the extraction of both spatial and temporal features, amalgamating them into spatio-temporal features. Some researchers [11; 12; 13] have suggested employing 2D convolution to extract spatial features frame by frame and store them in a buffer. Subsequently, new input frames are processed and their spatial features are combined with those in the buffer to construct spatio-temporal features for behavior recognition. Conversely, other researchers [14; 15; 16; 17] have advocated for the use of 3D convolution to extract spatio-temporal features directly from video clips for behavior recognition. The classical behavior recognition method based on 2D convolution is the spatio-temporal dual-stream CNN [18]. This approach utilizes optical flow to capture temporal information between video frames, and it has several variants, including the dual-stream network, TSN network [19], and I3D network [20], among others. Despite the enhancements in accuracy achieved by optical flow methods in behavior recognition, the substantial memory consumption and computational cost associated with extracting optical flow features preclude the attainment of end-to-end recognition. Instead of relying on optical flow for learning intricate temporal features, 3 Dconvolution-based behavior recognition introduces an additional temporal dimension compared to 2D convolution, facilitating end-to-end feature extraction and classification. The seminal approach in this domain is the C3D network [21], which extends the convolution kernel of VGGNet from a 3 \(\times\) 3 2D convolution to a 3 \(\times\) 3 \(\times\) 3D convolution. Despite its simplicity, the 3D convolutional kernel results in an exponential increase in the number of parameters and computational cost for the network. The algorithmic design of C3D marked a significant milestone in behavior recognition, and subsequent 3D networks have been developed, including the R3D [22] and P3D [23] algorithms. In the domain of agricultural behavior recognition, [PERSON] et al. introduced an enhanced ConvLSTM model [24] and an improved (2 + 1)D model [25] for the recognition of four types of agricultural behaviors in 2021 and 2022. Additionally, [PERSON] et al. devised a methodology for recognizing two types of agricultural behaviors using the YOLOv7-tiny model in 2023 [26]. However, the datasets used to train these models suffer from small-scale and missing behavior categories, which limits their practical applicability. Moreover, these models exhibit certain deficiencies in computational efficiency and feature extraction. These are: insufficient global feature extraction capability and inadequate spatio-temporal feature extraction when extracting agricultural labor images. This paper proposes an enhanced SlowFast network. The network incorporates a CoordAttention (CA) attention module to compensate for the shortcomings in CNN's global feature extraction ability.