Video-Based Analysis of Cattle Behaviors: Improved Classification Using FlowEQ Transform

Abstract

Cattle management plays a crucial role in determining the productivity of livestock farms. With the expansion of large-scale livestock operations, it has become increasingly impractical for livestock managers to rely on traditional visual observations for comprehensive monitoring of cattle behaviors, encompassing health and overall welfare. Consequently, the incorporation of automation technology in livestock management is emphasized. The objective of this study is the video-based identification of cattle behavior that can be utilized in automated cattle management systems. With a specific focus on behaviors closely associated with their management, the study employs deep learning-based action classification methods over the commonly used object detection. This approach enables the classification of intricate, repetitive, and slow behaviors that were challenging to detect. Furthermore, a novel method named FlowEQ transform was introduced, incorporating temporal information into the input data. This enhancement proved instrumental in providing valuable insights for inferring cattle behavior, resulting in an impressive 8% improvement in classification performance and achieving a high accuracy rate of 91.5%. The utilization of action classification and the introduction of the innovative FlowEQ transform mark a significant advancement in automated cattle management. This approach is poised to enhance the efficiency of behavior monitoring on livestock farms.