Evaluation of a novel computer vision-based livestock monitoring system to identify and track specific behaviors of individual nursery pigs within a group-housed environment.

Animal behavior is indicative of health status and changes in behavior can indicate health issues (i.e., illness, stress, or injury).

Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification.

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed.

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.

Smart watch RSSI localization and refinement for behavioral classification using laser-SLAM for mapping and fingerprinting.

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.

Physical activity discrimination improvement using accelerometers and wireless sensor network localization - biomed 2013.

Automating documentation of physical activity data (e.g., duration and speed of walking or propelling a wheelchair) into the electronic medical record (EMR) offers promise for improving efficiency of documentation and understanding of best practices in the rehabilitation and home health settings.

Stereoscopic vision-based robotic manipulator extraction method for enhanced soft tissue reconstruction.

The availability of digital stereoscopic video feedback on surgical robotic platforms allows for a variety of enhancements through the application of computer vision. Several of these enhancements, such as augmented reality and semi-automated surgery, benefit significantly from identification of the robotic manipulators within the field of view. A method is presented for the extraction of robotic manipulators from stereoscopic views of the operating field that uses a combination of marker tracking, inverse kinematics, and computer rendering.

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment.

Inexpensive, high-throughput, low maintenance systems for precise temporal and spatial measurement of mouse home cage behavior (including movement, feeding, and drinking) are required to evaluate products from large scale pharmaceutical design and genetic lesion programs. These measurements are also required to interpret results from more focused behavioral assays. We describe the design and validation of a highly-scalable, reliable mouse home cage behavioral monitoring system modeled on a previously described, one-of-a-kind system.

Real-time three-dimensional soft tissue reconstruction for laparoscopic surgery.

Background: Accurate real-time 3D models of the operating field have the potential to enable augmented reality for endoscopic surgery. A new system is proposed to create real-time 3D models of the operating field that uses a custom miniaturized stereoscopic video camera attached to a laparoscope and an image-based reconstruction algorithm implemented on a graphics processing unit (GPU).

Methods: The proposed system was evaluated in a porcine model that approximates the viewing conditions of in vivo surgery.

A compact high-definition low-cost digital stereoscopic video camera for rapid robotic surgery development.

Robotic surgical platforms require vision feedback systems, which often consist of low-resolution, expensive, single-imager analog cameras. These systems are retooled for 3D display by simply doubling the cameras and outboard control units. Here, a fully-integrated digital stereoscopic video camera employing high-definition sensors and a class-compliant USB video interface is presented.

Stereo image-based arm tracking for in vivo surgical robotics.

Motor-based tracking and image-based tracking are considered for three-dimensional in vivo tracking of the arms of a surgical robot during minimally invasive surgery. Accurate tracking is necessary for tele-medical applications and for the future automation of surgical procedures. An experiment is performed to compare the accuracy of the two methods, and results show that the positioning error of image-based tracking is significantly less than that of motor-based tracking.