Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning.

Background: Calorimetry is both expensive and obtrusive but provides the only way to accurately measure energy expenditure in daily living activities of any specific person, as different people can use different amounts of energy despite performing the same actions in the same manner. Deep learning video analysis techniques have traditionally required a lot of data to train; however, recent advances in few-shot learning, where only a few training examples are necessary, have made developing personalized models without a calorimeter a possibility.

Objective: The primary aim of this study is to determine which activities are most well suited to calibrate a vision-based personalized deep learning calorie estimation system for daily living activities.

Evaluating Cognitive Enrichment for Zoo-Housed Gorillas Using Facial Recognition.

The use of computer technology within zoos is becoming increasingly popular to help achieve high animal welfare standards. However, despite its various positive applications to wildlife in recent years, there has been little uptake of machine learning in zoo animal care. In this paper, we describe how a facial recognition system, developed using machine learning, was embedded within a cognitive enrichment device (a vertical, modular finger maze) for a troop of seven Western lowland gorillas () at Bristol Zoo Gardens, UK.

Perspectives in machine learning for wildlife conservation.

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge.

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications.

The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home.

Automated face detection for occurrence and occupancy estimation in chimpanzees.

Unlabelled: Surveying endangered species is necessary to evaluate conservation effectiveness. Camera trapping and biometric computer vision are recent technological advances. They have impacted on the methods applicable to field surveys and these methods have gained significant momentum over the last decade.

Automated Visual Fin Identification of Individual Great White Sharks.

This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first contour-based visual ID system in the field of animal biometrics.

Animal biometrics: quantifying and detecting phenotypic appearance.

Animal biometrics is an emerging field that develops quantified approaches for representing and detecting the phenotypic appearance of species, individuals, behaviors, and morphological traits. It operates at the intersection between pattern recognition, ecology, and information sciences, producing computerized systems for phenotypic measurement and interpretation. Animal biometrics can benefit a wide range of disciplines, including biogeography, population ecology, and behavioral research.