A Case Study of Trust Issues in Scientific Video Collections

In-situ video recording of underwater ecosystems is able to provide valuable information for biology research and natural resources management, e.g. changes in species abundance. Searching the videos manually, however, requires costly human effort. Our video analysis tool supports the key task of counting different species of fish, allowing marine biologists to query the video collection without watching the videos. To be suitable for scientific research on changes in species abundance, the video data must include data provenance information that reflects the potential biases introduced through the video processing.In order to trust the analyses made by the system, we need to provide expert users with sufficient information to allow them to interpret these potential biases. We conducted two user studies to design a user interface that includes data provenance information. Our qualitative analysis discusses the support for understanding the reliability of video analysis, and trusting the results it produces. Our main finding is that disclosing details about the video processing and provenance data allows biologists to compare the results with their traditional statistical methods, thus increasing their trust in the results

Interactive Visualization of Video Data for Fish Population Monitoring

The recent use of computer vision techniques for monitoring ecosystems has opened new perspectives for marine ecology research. These techniques can extract information about fish populations from in-situ cameras, without requiring ecologists to watch the videos. However, they inherently introduce uncertainty since a

A Video Processing and Data Retrieval Framework for Fish Population Monitoring

In this work we present a framework for fish population monitoring through the analysis of underwater videos. We specifically focus on the user information needs, and on the dynamic data extraction and retrieval mechanisms that support them. Sophisticated though a software tool may be, it is ultimately important that its interface satisfies users' actual needs and that users can easily focus on the specific data of interest. In the case of fish population monitoring, marine biologists have to interact with a system which not only provides information from a biological point of view, but also offers instruments to let them guide the video processing task for both video and algorithm selection. This paper aims at describing the system's underlying video processing and workflow low-level details, and their connection to the user interface for on-demand data retrieval by biologists

A video processing and data retrieval framework for fish population monitoring

htmlabstractIn this work we present a framework for fish population monitoring through the analysis of underwater videos. We specifically focus on the user information needs, and on the dynamic data extraction and retrieval mechanisms that support them. Sophisticated though a software tool may be, it is ultimately important that its interface satisfies users' actual needs and that users can easily focus on the specific data of interest. In the case of fish population monitoring, marine biologists have to interact with a system which not only provides information from a biological point of view, but also offers instruments to let them guide the video processing task for both video and algorithm selection. This paper aims at describing the system's underlying video processing and workflow low-level details, and their connection to the user interface for on-demand data retrieval by biologists

Uncertainty-Aware Estimation of Population Abundance using Machine Learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classication based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classication. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is ne

Multifactorial Uncertainty Assessment for Monitoring Population Abundance using Computer Vision

Computer vision enables in-situ monitoring of animal populations at a lower cost and with less ecosystem disturbance than with human observers. However, computer vision uncertainty may not be fully understood by end-users, and the uncertainty assessments performed by technology experts may not fully address end-user needs. This knowledge gap can yield misinterpretations of computer vision data, and trust issues impeding the transfer of valuable technologies. We bridge this gap with a user-centered analysis of the uncertainty issues. Key uncertainty factors, and their interactions, are identified from the perspective of a core task in ecology research and beyond: counting individuals from different classes. We highlight factors for which uncertainty assessment methods are currently unavailable. The remaining uncertainty assessment methods are not interoperable. Hence it is currently difficult to assess the combined results of multiple uncertainty factors, and their impact on end-user counting tasks. We propose a framework for assessing the multifactorial uncertainty propagation along the data processing pipeline. It integrates methods from both computer vision and ecology domains, and aims at supporting the statistical analysis of abundance trends for population monitoring. Our typology of uncertainty factors and our assessment methods were drawn from interviews with marine ecology and computer vision experts, and from prior work for a fish monitoring application. Our findings contribute to enabling scientific research based on computer vision