Extracting Accurate Long-Term Behavior Changes from a Large Pig Dataset

Visual observation of uncontrolled real-world behavior leads to noisy observations, complicated by occlusions, ambiguity, variable motion rates, detection and tracking errors, slow transitions between behaviors, etc. We show in this paper that reliable estimates of long-term trends can be extracted given enough data, even though estimates from individual frames may be noisy. We validate this concept using a new public dataset of approximately 20+ million daytime pig observations over 6 weeks of their main growth stage, and we provide annotations for various tasks including 5 individual behaviors. Our pipeline chains detection, tracking and behavior classification combining deep and shallow computer vision techniques. While individual detections may be noisy, we show that long-term behavior changes can still be extracted reliably, and we validate these results qualitatively on the full dataset. Eventually, starting from raw RGB video data we are able to both tell what pigs main daily activities are, and how these change through time

Robust individual pig tracking

The locations of pigs in the group housing enable activity monitoring and improve animal welfare. Vision-based methods for tracking individual pigs are noninvasive but have low tracking accuracy owing to long-term pig occlusion. In this study, we developed a vision-based method that accurately tracked individual pigs in group housing. We prepared and labeled datasets taken from an actual pig farm, trained a faster region-based convolutional neural network to recognize pigs’ bodies and heads, and tracked individual pigs across video frames. To quantify the tracking performance, we compared the proposed method with the global optimization (GO) method with the cost function and the simple online and real-time tracking (SORT) method on four additional test datasets that we prepared, labeled, and made publicly available. The predictive model detects pigs’ bodies accurately, with F1-scores of 0.75 to 1.00, on the four test datasets. The proposed method achieves the largest multi-object tracking accuracy (MOTA) values at 0.75, 0.98, and 1.00 for three test datasets. In the remaining dataset, the proposed method has the second-highest MOTA of 0.73. The proposed tracking method is robust to long-term occlusion, outperforms the competitive baselines in most datasets, and has practical utility in helping to track individual pigs accurately

Multi-Pig Part Detection and Association with a Fully-Convolutional Network

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download

Panoptic Segmentation of Individual Pigs for Posture Recognition

Behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Systems based on computer vision in particular have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown excellent results. Object and keypoint detector have frequently been used to detect individual animals. Despite promising results, bounding boxes and sparse keypoints do not trace the contours of the animals, resulting in a lot of information being lost. Therefore, this paper follows the relatively new approach of panoptic segmentation and aims at the pixel accurate segmentation of individual pigs. A framework consisting of a neural network for semantic segmentation as well as different network heads and postprocessing methods will be discussed. The method was tested on a data set of 1000 hand-labeled images created specifically for this experiment and achieves detection rates of around 95% (F1 score) despite disturbances such as occlusions and dirty lenses

Automatic recognition of feeding and foraging behaviour in pigs using deep learning

Highlights• An automated detection method of pig feeding and foraging behaviour was developed.• The automated method is based on convolutional deep neural networks.• The automated method does not rely on pig tracking to estimate behaviours.• Detection of feeding behaviour is highly accurate (99.4%) and fast (0.02 sec/image).• The robust method can be applied under different husbandry/ management conditions.Automated, vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare status. In commercial settings, automatic recording of feeding behaviour remains a challenge due to problems of variation in illumination, occlusions and similar appearance of different pigs. Additionally, such systems, which rely on pig tracking, often overestimate the actual time spent feeding, due to the inability to identify and/or exclude non-nutritive visits (NNV) to the feeding area. To tackle these problems, we have developed a robust, deep learning-based feeding detection method that (a) does not rely on pig tracking and (b) is capable of distinguishing between feeding and NNV for a group of pigs. We first validated our method using video footage from a commercial pig farm, under a variety of settings. We demonstrate the ability of this automated method to identify feeding and NNV behaviour with high accuracy (99.4% ± 0.6%). We then tested the method's ability to detect changes in feeding and NNV behaviours during a planned period of food restriction. We found that the method was able to automatically quantify the expected changes in both feeding and NNV behaviours. Our method is capable of monitoring robustly and accurately the feeding behaviour of groups of commercially housed pigs, without the need for additional sensors or individual marking. This has great potential for application in the early detection of health and welfare challenges of commercial pigs

Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps. This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks. Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets. For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold. Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data. Adivser: Lance C. Pére

Tracking of group‐housed pigs using multi‐ellipsoid expectation maximisation

Maintaining the health and well‐being of animals is critical to the efficiency and profitability of livestock operations. However, it can be difficult to monitor the health of animals in large group‐housed settings without the assistance of technology. This study presents a system that uses depth images to continuously track individual pigs in a group‐housed environment. It is an alternative to traditional manual observation used by both researchers and producers for the analysis of animal activities and behaviours. The tracking method used by the system exploits the consistent shape and fixed number of the targets in the environment by applying expectation maximisation as a policy for fitting an ellipsoid to each target. Results demonstrate that the system can maintain the correct positions and orientations of 15 group‐housed pigs for an average of 19.7 min between failure events

Power from the people: the empowerment of distributed generation of solar electricity for rural communities in Malaysia

This paper describes the decreasing energy security in Malaysia and the likely impact on maintaining power supplies to low income groups. The most vulnerable group is the low-income people in the rural areas, who have limited access to generate their own power supplies. The paper reviews the potential of distributed generation (DG) using photovoltaics as a means of mitigating this problem. Examples from other countries are reviewed and alternative methods of funding PV installations are discussed. Strategies such as community-based approach and innovative financing scheme will be introduced and discussed. The main objective is to utilize solar energy as the main energy resources for generating electricity and places rural people as the main stakeholder to deploy the strategic model. This model is also ideal to be integrated with the distributed generation (DG) system as one of the key components in developing a suitable energy policy that can helps to sustain the energy development of rural community in the future. The paper concludes that distributed generation (DG) is feasible and that innovative funding schemes are required based on local knowledge