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.

Fragile X syndrome (FXS) is a common inherited disorder that significantly impacts family and patient day-to-day living across the entire lifespan. The childhood and adolescent behavioral consequences of FXS are well-appreciated. However, there are significantly fewer studies (except those examining psychiatric comorbidities) assessing behavioral phenotypes seen in adults with FXS. Mice engineered with a genetic lesion of Fmr1 recapitulate important molecular and neuroanatomical characteristics of FXS, and provide a means to evaluate adult behavioral phenotypes associated with FXS. We give the first description of baseline behaviors including feeding, drinking, movement, and their circadian rhythms; all observed over 16 consecutive days following extensive environmental habituation in adult Fmr1-KO mutant mice. We find no genotypic changes in mouse food ingestion, feeding patterns, metabolism, or circadian patterns of movement, feeding, and drinking. After habituation, Fmr1-KO mice demonstrate significantly less daily movement during their active phase (the dark cycle). However, Fmr1-KO mice have more bouts of activity during the light cycle compared to wildtypes. In addition, Fmr1-KO mice demonstrate significantly less daily water ingestion during the circadian dark cycle, and this reduction in water intake is accompanied by a decrease in the amount of water ingested per lick. The observed water ingestion and circadian phenotypes noted in Fmr1-KO mice recapitulate known clinical aspects previously described in FXS. The finding of decreased movement in Fmr1-KO mice is novel, and suggests a dissociation between baseline and novelty-evoked activity for Fmr1-KO mice.

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. 2 Background Approaches to MOT of animals often begin by segmenting the animals from both the background and from one another. For pigs,

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As a first step toward building a smart home behavioral monitoring system capable of classifying a wide variety of human behavior, a wireless sensor network (WSN) system is presented for RSSI localization. The low-cost, non-intrusive system uses a smart watch worn by the user to broadcast data to the WSN, where the strength of the radio signal is evaluated at each WSN node to localize the user. A method is presented that uses simultaneous localization and mapping (SLAM) for system calibration, providing automated fingerprinting associating the radio signal strength patterns to the user's location within the living space. To improve the accuracy of localization, a novel refinement technique is introduced that takes into account typical movement patterns of people within their homes. Experimental results demonstrate that the system is capable of providing accurate localization results in a typical living space.