Detecting Biological Locomotion in Video: A Computational Approach

• URL: http://arxiv.org/abs/2105.12661v1
• Date: Wed, 26 May 2021 16:19:23 GMT
• Title: Detecting Biological Locomotion in Video: A Computational Approach
• Abstract summary: We present a computational approach to detect biolocomotion in unprocessed video. We exploit this key trait of positional advance with asymmetric oscillation along with differences in an object's common motion. An algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion.
• Score: 13.135234328352885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Animals locomote for various reasons: to search for food, find suitable habitat, pursue prey, escape from predators, or seek a mate. The grand scale of biodiversity contributes to the great locomotory design and mode diversity. Various creatures make use of legs, wings, fins and other means to move through the world. In this report, we refer to the locomotion of general biological species as biolocomotion. We present a computational approach to detect biolocomotion in unprocessed video. Significantly, the motion exhibited by the body parts of a biological entity to navigate through an environment can be modeled by a combination of an overall positional advance with an overlaid asymmetric oscillatory pattern, a distinctive signature that tends to be absent in non-biological objects in locomotion. We exploit this key trait of positional advance with asymmetric oscillation along with differences in an object's common motion (extrinsic motion) and localized motion of its parts (intrinsic motion) to detect biolocomotion. An algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion. An alternative algorithm, based on generic features combined with learning is assembled out of components from allied areas of investigation, also is presented as a basis of comparison. A novel biolocomotion dataset encompassing a wide range of moving biological and non-biological objects in natural settings is provided. Also, biolocomotion annotations to an extant camouflage animals dataset are provided. Quantitative results indicate that the proposed algorithm considerably outperforms the alternative approach, supporting the hypothesis that biolocomotion can be detected reliably based on its distinct signature of positional advance with asymmetric oscillation and extrinsic/intrinsic motion dissimilarity.

Related papers

• BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval [0.0]
  BioKERN is a multimodal representation-learning framework that incorporates biological structure as an explicit, learnable inductive bias.<n>Across Mouse Brain Visium and Human Liver GSE240429, BioKERN consistently improves biological-neighborhood retrieval over BLEEP.
  arXiv  Detail & Related papers  (2026-08-25T17:04:16Z)
• Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction [4.726777092009554]
  BioModule is a lightweight plug-in temporal transformer that attaches downstream of any 3D pose estimator.<n>BioModule is estimator-agnostic and requires no modification of the upstream pose model.<n>We benchmark BioModule across seven state-of-the-art 3D pose estimators.
  arXiv  Detail & Related papers  (2026-07-09T17:31:16Z)
• A non-invasive video-based method for individual identification of wildlife using gait dynamics [21.468912995256698]
  This paper presents a fully automated video-based pipeline for wildlife gait analysis and individual identification.<n>The proposed pipeline uses Segment Anything Model 3 (SAM3) to generate high-quality RGB and binary silhouette masks.<n>Cosine is then used to compare gait signatures, enabling similarity-based clustering of individuals without reliance on physical or invasive markings.
  arXiv  Detail & Related papers  (2026-07-05T21:47:21Z)
• Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot [56.377161933570335]
  Humans naturally exploit a "weightless" state during non-self-stabilizing (NSS) motions.<n>Inspired by this biological mechanism, we propose a weightlessness-state auto-labeling strategy for dataset annotation.<n>Our work bridges the gap between precise trajectory tracking and adaptive environmental interaction, offering a biologically-inspired solution for contact-rich humanoid control.
  arXiv  Detail & Related papers  (2026-04-23T07:10:05Z)
• Automatic Image-Level Morphological Trait Annotation for Organismal Images [11.844505008466884]
  We introduce a trait annotation pipeline that localizes salient regions and uses vision-language prompting to generate interpretable trait descriptions.<n>We construct Bioscan-Traits, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M.
  arXiv  Detail & Related papers  (2026-04-02T04:55:52Z)
• Behavioural Classification in C. elegans: a Spatio-Temporal Analysis of Locomotion [0.0]
  The 1mm roundworm C. elegans is a model used in many sub-areas of biology.<n>Several methods have been proposed to simulate the worm behaviour.
  arXiv  Detail & Related papers  (2025-09-30T09:06:15Z)
• Hyperbolic Multimodal Representation Learning for Biological Taxonomies [23.639218053531962]
  Taxonomic classification in biodiversity research involves organizing biological specimens into structured hierarchies based on evidence.<n>We investigate whether hyperbolic networks can provide a better embedding space for such hierarchical models.<n>Our method embeds multimodal inputs into a shared hyperbolic space using contrastive and a novel stacked entailment-based objective.
  arXiv  Detail & Related papers  (2025-08-22T18:52:50Z)
• Flow Matching Meets Biology and Life Science: A Survey [65.2146737141455]
  Flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling.<n>This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains.
  arXiv  Detail & Related papers  (2025-07-23T17:44:29Z)
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning [51.341003735575335]
  We find emergent behaviors in biological vision models via large-scale contrastive vision-language training.<n>We train BioCLIP 2 on TreeOfLife-200M to distinguish different species.<n>We identify emergent properties in the learned embedding space of BioCLIP 2.
  arXiv  Detail & Related papers  (2025-05-29T17:48:20Z)
• BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks [3.097167420381722]
  We created a multimodal dataset of motion-related questions and answers spanning a range of tasks.<n>We developed BiomechGPT, a multimodal biomechanics-language model, on this dataset.<n>Our results show that BiomechGPT demonstrates high performance across a range of tasks such as activity recognition, identifying movement impairments, diagnosis, scoring clinical outcomes, and measuring walking.
  arXiv  Detail & Related papers  (2025-05-24T02:15:23Z)
• BioMoDiffuse: Physics-Guided Biomechanical Diffusion for Controllable and Authentic Human Motion Synthesis [21.750804738752105]
  This paper introduces BioMoDiffuse, a novel biomechanics-aware diffusion framework. It features three key innovations: (1) A lightweight biodynamic network that integrates muscle electromyography (EMG) signals with acceleration constraints; (2) A physics-guided diffusion process that incorporates real-time biomechanical verification via modified Euler-Lagrange equations; and (3) A decoupled control mechanism that allows independent regulation of motion speed and semantic context.
  arXiv  Detail & Related papers  (2025-03-08T10:22:36Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems [59.117526206317116]
  We show that CELL can adaptively evolve into different models for different tasks and data. Experiments on four real-world datasets demonstrate that CELL significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-29T02:35:23Z)
• Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity [25.488181126364186]
  This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. We apply our method to grand biological challenges, such as data integration in single-cell genomics. Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest.
  arXiv  Detail & Related papers  (2023-07-02T12:52:41Z)
• The scaling of goals via homeostasis: an evolutionary simulation, experiment and analysis [0.0]
  We propose that evolution pivoted the collective intelligence of cells during morphogenesis into behavioral intelligence by scaling up the goal states at the center of homeostatic processes. We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve its target morphology. We propose that this system is a first step toward a quantitative understanding of how evolution scales minimal goal-directed behavior (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.
  arXiv  Detail & Related papers  (2022-11-15T21:48:44Z)
• Robust Analytics for Video-Based Gait Biometrics [1.066048003460524]
  This thesis discusses both hard and soft biometric characteristics of gait. It shows how to identify gender based on gait alone through the Posed-Based Voting scheme. The mapping can be improved in a smaller population using Bayesian Thresholding.
  arXiv  Detail & Related papers  (2021-11-12T11:47:35Z)
• Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales [97.41394631426678]
  Recent research showed the promise of machine learning tools for analyzing acoustic communication in nonhuman species. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales. The technological capabilities developed are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research.
  arXiv  Detail & Related papers  (2021-04-17T18:39:22Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots [58.720142291102135]
  Spiking Central Pattern Generator generates different locomotion patterns driven by an external stimulus. The locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input.
  arXiv  Detail & Related papers  (2021-01-24T12:44:38Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.