EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board

Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this light-weight method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.

Automated summary

In this study, a camera-based automatic method for counting the number of pigs passing through a counting zone on a large-scale pig farm is proposed. The method utilizes deep-learning-based video object detection and tracking techniques and is able to achieve real-time and accurate pig counting. Experimental results demonstrate that the method achieves an accuracy of 99.44% and real-time execution.