Video-Based Pig Counting Using Skeleton Tracking
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Solution Overview
Problem
Manual counting of pigs in large pig farming facilities is labor-intensive and costly, necessitating an efficient and automated method for tracking the number of pigs in different growth stages and detecting any missing animals.
Innovation Solution
A video-based system using a computing device and imaging device equipped with a neural network to detect keypoints, track animal skeletons, and differentiate between activated and deactivated zones for accurate pig counting, employing convolutional neural networks (CNNs) to process images and provide a count of animals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual counting methods are used to count pigs in large farming facilities, then the counting process can be performed with simple equipment, but the labor cost and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical counting with an automated video-based system using imaging devices and neural networks. The system captures video footage of pigs in farming facilities and uses deep learning algorithms to automatically detect, track, and count individual pigs, eliminating the need for manual labor while significantly improving counting efficiency and accuracy.
2Measurement precision
If traditional video-based counting methods are used without skeleton tracking, then the system is simpler, but the accuracy decreases in crowded and occluded environments
Solution Approach 1:
The patent segments the pig detection process into multiple components: keypoint detection for identifying body parts, skeleton construction for representing individual pig structures, and trajectory tracking for following pig movements. This segmentation allows the system to accurately distinguish individual pigs even in crowded and occluded environments by tracking their unique skeletal trajectories through the video sequence.
3Measurement precision
If the system tracks all pig movements continuously, then the counting accuracy is maintained, but the computational energy consumption increases
Solution Approach 1:
The patent implements periodic action by dividing the video sequence into frames and performing skeleton tracking at discrete time intervals rather than continuously. The system processes each frame to detect keypoints and construct skeletons, then tracks trajectories between frames, reducing computational energy consumption while maintaining counting accuracy through periodic analysis of pig movements.
Data Source
AI summary
A method and a system for counting pigs in a pig house. The system includes a computing device and an imaging device. The computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: receive images captured from one end to the other end of the house; detect keypoints in the images using a neural network; identify pig skeletons by associating several related keypoints; track the skeletons in the images to obtain trajectories; divide each image into an activated zone and a deactivated zone; designate a spatial value of 0 for the skeletons in the activated zone and a spatial value of 1 for the skeletons in the deactivated zone; summating first order difference of the spatial values for each trajectory to obtain a trajectory count; and add the trajectory counts to obtain pig count.