Biology subjects

Beleyur, T.

Publications and source records attributed to Beleyur, T..

4 recordsLinked to original sources

bioRxiv2023

A target-free approach to estimate thermal camera pose in LiDAR scenes of feature-deficient environments

Modern animal behaviourists are able to collect vast amounts of data from multiple sensors. Sensor fusion however still remains a challenge given the unique sensor types biologists use. Here we study thermal camera-LiDAR alignment. While LiDAR-RGB scene alignment is well-established in feature-rich scenes with reliable inter-camera correspondences, thermal scenes are often captured with low-resolution devices and suffer from halo and history effects, and thus require sensor-specific algorithms. To deal with this, we present the depth-map correspondence (DMCP) algorithm. DMCP is a semi-automatic algorithm to fuse Li-DAR with feature-deficient thermal scenes without explicit calibration objects. The user annotates at least 4 corresponding points on the thermal image and a depth map of the LiDAR scene, which allows us to compute the position and orientation of the thermal camera within the scene. We quantify the accuracy of alignment using 2D reprojection error and 3D nearest-neighbour distances of known world points across experimental trials. A median reprojection error of 7.7 (95%ile: 3.7-23.5) pixels was achieved. Known objects lying on the LiDAR mesh showed a median nearest-neighbour mesh distance of 0.12 m (95%ile: 0.007-5.61 m). We present the first such alignment of feature-deficient thermal scenes and LiDAR data. DMCP has two advantages 1) it is computationally straightforward to implement and 2) requires minimal user annotation to work. DMCP could be broadly applied to any type of mesh-image alignment problem. Finally we provide DMCP and the Ushichka dataset as a baseline for further research on the challenging problem of subterranean thermal-LiDAR alignment.

animal behavior and cognition↗

bioRxiv2021

itsfm, an open-source package to reliably segment and measure sounds by frequency modulation

Analysing animal vocalisations in detail provides insights into the biomechanics, decision making and sensory processes behind their behaviours. Echolocating bats, and in particular, the CF-FM calls of high-duty cycle bats serve as a convenient model system to illustrate this point. The CF component in the CF-FM call is used for prey detection and the FM component is used in target ranging. According to the behavioural context at hand such as flight with conspecifics or prey capture, bats choose to increase the duration, intensity or spectral range of the components differently. Studying the call component alterations requires an objective methodology that first segments the components and then allows measurements on them. Studies till now have segmented the call components manually, or automatically using what I term the peak-frequency method. Manual segmentation is error prone, while the peak-frequency method requires on-axis recordings for good results. Despite multiple papers using a peak-frequency based segmentation, there remain no publicly available software implementations. itsfm is an open-source package that fills this gap with two implemntations that can segment CF-FM calls, one of them being an implementation of the peak-percentage method. itsfm additionally introduces the pseudo-Wigner-Ville distribution (PWVD) method for call segmentation, thus allowing the segmentation of calls captured under a wider variety of recording conditions. I create a synthetic dataset and assess the performance of the PWVD method and the peak-frequency method. The PWVD performs consistently well in call component segmentation in comparison to the peak-percentage method. I also discuss the supporting methods in the itsfm package that can help the further automatic segmentation, measurement and analysis of sounds. Though originally developed for the segmentation and measurement of CF-FM bat calls, the methods in itsfm are speciesagnostic, and may be used for vocalisations of any type.

animal behavior and cognition↗

bioRxiv2020

tacost: Testing and simulating the performance of acoustic tracking systems

tacost is a Python package to allow the testing of acoustic tracking systems. While many microphone array systems have been characterised analytically and experimentally - these are time-intensive methods. tacost provides a simulation based framework to rapidly assess the tracking behaviour of multiple array geometries, and the dissection of other relevant parameters. This paper explains briefly the design of the package and highlights two example use cases in which the tracking accuracy of different microphone geometries are characterised.

animal behavior and cognition↗

bioRxiv2019

Active sensing in groups: (what) do bats hear in the sonar cocktail party nightmare?

Active sensing animals perceive their surroundings by emitting probes of energy and analyzing how the environment modulates these probes. However, the probes of conspecifics can jam active sensing, which should cause problems for groups of active sensing animals. This problem was termed the cocktail party nightmare for echolocating bats: as bats listen for the faint returning echoes of their loud calls, these echoes will be masked by the loud calls of other close-by bats. Despite this problem, many bats echolocate in groups and roost socially. Here, we present a biologically parametrized framework to quantify echo detection in groups. Incorporating known properties of echolocation, psychoacoustics, spatial acoustics and group flight, we quantify how well bats flying in groups can detect each other despite jamming. A focal bat in the center of a group can detect neighbors for group sizes of up to 100 bats. With increasing group size, fewer and only the closest and frontal neighbors are detected. Neighbor detection is improved for longer call intervals, shorter call durations, denser groups and more variable flight and sonar beam directions. Our results provide the first quantification of the sensory input of echolocating bats in collective group flight, such as mating swarms or emergences. Our results further generate predictions on the sensory strategies bats may use to reduce jamming in the cocktail party nightmare. Lastly, we suggest that the spatially limited sensory field of echolocators leads to limited interactions within a group, so that collective behavior is achieved by following only nearest neighbors. SIGNIFICANCE STATEMENTClose-by active sensing animals may interfere with each other. We investigated if and what many echolocators fly in a group hear - can they detect each other after all? We modelled acoustic and physical properties in group echolocation to quantify neighbor detection probability as group size increases. Echolocating bats can detect at least one of their closest neighbors per call up to group sizes of even 100 bats. Call parameters such as call rate and call duration play a strong role in how much echolocators in a group interfere with each other. Even when many bats fly together, they are indeed able to detect at least their nearest frontal neighbors - and this prevents them from colliding into one another.

animal behavior and cognition↗