Fish telemetry and positioning from an autonomous underwater vehicle (AUV)

We explored telemetry of transmitter tagged fishes from an autonomous underwater vehicle with a hydrophone/ datalogger processing code-division-multiple- access acoustic signals. Geolocation estimates used synthetic aperture and relative sound strength mapping. Signal reception patterns from tagged Atlantic sturgeon were similar to that of moored reference tags but those from tagged winter flounder were reduced in range due to burying behavior.Peer Reviewe

A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna

The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction) is accompanied by species identification from animals' outlines by Fourier Descriptors and Standard K-Nearest Neighbours methods. One-week footage from a permanent video-station located at 1,100 m depth in Sagami Bay (Central Japan) was analysed. Out of 150,000 frames (1 per 4 s), a subset of 10.000 was analyzed by a trained operator to increase the efficiency of the automated procedure. Error estimation of the automated and trained operator procedure was computed as a measure of protocol performance. Three displacing species were identified as the most recurrent: Zoarcid fishes (eelpouts), red crabs (Paralomis multispina), and snails (Buccinum soyomaruae). Species identification with KNN thresholding produced better results in automated motion detection. Results were discussed assuming that the technological bottleneck is to date deeply conditioning the exploration of the deep-sea

Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning

Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, surveillance, robotics, and others. In the model-based Bayesian setting, there are conjugate priors that enable us to express the multi-object posterior in closed form, which could theoretically provide Bayes-optimal estimates. However, the posterior involves a super-exponential growth of the number of hypotheses over time, forcing state-of-the-art methods to resort to approximations for remaining tractable, which can impact their performance in complex scenarios. Model-free methods based on deep-learning provide an attractive alternative, as they can, in principle, learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available. In this paper, we propose a high-performing deep-learning method for MTT based on the Transformer architecture and compare it to two state-of-the-art Bayesian filters, in a setting where we assume the correct model is provided. Although this gives an edge to the model-based filters, it also allows us to generate unlimited training data. We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime. The code for all our implementations is made available at https://github.com/JulianoLagana/MT3 .Comment: 8 pages, 4 figure

Multiple Fish Tracking via Viterbi Data Association for Low-Frame-Rate Underwater Camera Systems †

Abstract-Non-extractive fish abundance estimation with the aid of visual analysis has drawn increasing attention. Low frame rate and variable illumination in the underwater environment, however, makes conventional tracking methods unreliable. In this paper, a robust multiple fish tracking system for low-framerate underwater stereo cameras is proposed. With the result of fish segmentation, a computationally efficient block-matching method is applied to perform successful stereo matching. A multiple-feature matching cost function is utilized to give a simple but effective metric for finding the temporal match of each target. Built upon reliable stereo matching, a multipletarget tracking algorithm via the Viterbi data association is developed to overcome the poor motion continuity of targets. Experimental results show that an accurate underwater live fish tracking result with stereo cameras is achieved

The role of first- and second-order stimulus features for human overt attention

When processing complex visual input, human observers sequentially allocate their attention to different subsets of the stimulus. What are the mechanisms and strategies that guide this selection process? We investigated the influence of various stimulus features on human overt attention—that is, attention related to shifts of gaze with natural color images and modified versions thereof. Our experimental modifications, systematic changes of hue across the entire image, influenced only the global appearance of the stimuli, leaving the local features under investigation unaffected. We demonstrated that these modifications consistently reduce the subjective interpretation of a stimulus as "natural” across observers. By analyzing fixations, we found that first-order features, such as luminance contrast, saturation, and color contrast along either of the cardinal axes, correlated to overt attention in the modified images. In contrast, no such correlation was found in unmodified outdoor images. Second-order luminance contrast ("texture contrast”) correlated to overt attention in all conditions. However, although none of the second-order color contrasts were correlated to overt attention in unmodified images, one of the second-order color contrasts did exhibit a significant correlation in the modified images. These findings imply, on the one hand, that higher-order bottom-up effects—namely, those of second-order luminance contrast—may partially account for human overt attention. On the other hand, these results also demonstrate that global image properties, which correlate to the subjective impression of a scene being "natural,” affect the guidance of human overt attentio