A dataset of ant colonies motion trajectories in indoor and outdoor
scenes for social cluster behavior study
- URL: http://arxiv.org/abs/2204.04380v1
- Date: Sat, 9 Apr 2022 03:49:55 GMT
- Title: A dataset of ant colonies motion trajectories in indoor and outdoor
scenes for social cluster behavior study
- Authors: Meihong Wu, Xiaoyan Cao, Xiaoyu Cao, Shihui Guo
- Abstract summary: In this paper, we collect 10 videos of ant colonies from different indoor and outdoor scenes.
In all 5354 frames, the location information and the identification number of each ant are recorded for a total of 712 ants and 114112 annotations.
It is hoped that this dataset will contribute to a deeper exploration on the behavior of the ant colony.
- Score: 13.391307807956675
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motion and interaction of social insects (such as ants) have been studied by
many researchers to understand the clustering mechanism. Most studies in the
field of ant behavior have only focused on indoor environments, while outdoor
environments are still underexplored. In this paper, we collect 10 videos of
ant colonies from different indoor and outdoor scenes. And we develop an image
sequence marking software named VisualMarkData, which enables us to provide
annotations of ants in the video. In all 5354 frames, the location information
and the identification number of each ant are recorded for a total of 712 ants
and 114112 annotations. Moreover, we provide visual analysis tools to assess
and validate the technical quality and reproducibility of our data. It is hoped
that this dataset will contribute to a deeper exploration on the behavior of
the ant colony.
Related papers
- Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.
Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.
Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
arXiv Detail & Related papers (2024-10-14T17:22:55Z) - Meerkat Behaviour Recognition Dataset [3.53348643468069]
We introduce a large meerkat behaviour recognition video dataset with diverse annotated behaviours.
This dataset includes videos from two positions within the meerkat enclosure at the Wellington Zoo (Wellington, New Zealand)
arXiv Detail & Related papers (2023-06-20T06:50:50Z) - Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location.
Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets.
We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
arXiv Detail & Related papers (2023-06-05T03:36:01Z) - Self-organizing nest migration dynamics synthesis for ant colony systems [0.0]
In study, we synthesize a novel dynamical approach for ant colonies enabling them to migrate to new nest sites in a self-organizing fashion.
We first segment the edges of the graph of ants' pathways. Then, each segment, attributed to its own pheromone profile, may host an ant.
Thanks to this segment-wise edge formulation, ants have more selection options in the course of their pathway determination.
arXiv Detail & Related papers (2022-10-08T09:16:16Z) - MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations
of Behavior [28.878568752724235]
We introduce MABe22, a benchmark to assess the quality of learned behavior representations.
This dataset is collected from a variety of biology experiments.
We test self-supervised video and trajectory representation learning methods to demonstrate the use of our benchmark.
arXiv Detail & Related papers (2022-07-21T15:51:30Z) - Animal Kingdom: A Large and Diverse Dataset for Animal Behavior
Understanding [4.606145900630665]
We create a large and diverse dataset, Animal Kingdom, that provides multiple annotated tasks.
Our dataset contains 50 hours of annotated videos to localize relevant animal behavior segments.
We propose a Collaborative Action Recognition (CARe) model that learns general and specific features for action recognition with unseen new animals.
arXiv Detail & Related papers (2022-04-18T02:05:15Z) - Video Action Detection: Analysing Limitations and Challenges [70.01260415234127]
We analyze existing datasets on video action detection and discuss their limitations.
We perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect.
Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.
arXiv Detail & Related papers (2022-04-17T00:42:14Z) - Swarm behavior tracking based on a deep vision algorithm [5.070542698701158]
We propose a detection and tracking framework for multi-ant tracking in the videos.
Our method runs 6-10 times faster than existing methods for insect tracking.
arXiv Detail & Related papers (2022-04-07T09:32:12Z) - Weakly Supervised Human-Object Interaction Detection in Video via
Contrastive Spatiotemporal Regions [81.88294320397826]
A system does not know what human-object interactions are present in a video as or the actual location of the human and object.
We introduce a dataset comprising over 6.5k videos with human-object interaction that have been curated from sentence captions.
We demonstrate improved performance over weakly supervised baselines adapted to our annotations on our video dataset.
arXiv Detail & Related papers (2021-10-07T15:30:18Z) - ASOD60K: Audio-Induced Salient Object Detection in Panoramic Videos [79.05486554647918]
We propose PV-SOD, a new task that aims to segment salient objects from panoramic videos.
In contrast to existing fixation-level or object-level saliency detection tasks, we focus on multi-modal salient object detection (SOD)
We collect the first large-scale dataset, named ASOD60K, which contains 4K-resolution video frames annotated with a six-level hierarchy.
arXiv Detail & Related papers (2021-07-24T15:14:20Z) - Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark [97.07865343576361]
We construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd.
We annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes.
We design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds.
arXiv Detail & Related papers (2021-05-06T04:46:14Z)
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.