BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos

• URL: http://arxiv.org/abs/2405.17698v3
• Date: Mon, 3 Jun 2024 23:24:39 GMT
• Title: BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos
• Authors: Isla Duporge, Maksim Kholiavchenko, Roi Harel, Scott Wolf, Dan Rubenstein, Meg Crofoot, Tanya Berger-Wolf, Stephen Lee, Julie Barreau, Jenna Kline, Michelle Ramirez, Charles Stewart,
• Abstract summary: This study presents a novel dataset from drone videos for baboon detection, tracking, and behavior recognition. The baboon detection dataset was created by manually annotating all baboons in drone videos with bounding boxes. The behavior recognition dataset was generated by converting tracks into mini-scenes, a video subregion centered on each animal.
• Score: 0.8074955699721389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using drones to track multiple individuals simultaneously in their natural environment is a powerful approach for better understanding group primate behavior. Previous studies have demonstrated that it is possible to automate the classification of primate behavior from video data, but these studies have been carried out in captivity or from ground-based cameras. To understand group behavior and the self-organization of a collective, the whole troop needs to be seen at a scale where behavior can be seen in relation to the natural environment in which ecological decisions are made. This study presents a novel dataset from drone videos for baboon detection, tracking, and behavior recognition. The baboon detection dataset was created by manually annotating all baboons in drone videos with bounding boxes. A tiling method was subsequently applied to create a pyramid of images at various scales from the original 5.3K resolution images, resulting in approximately 30K images used for baboon detection. The tracking dataset is derived from the detection dataset, where all bounding boxes are assigned the same ID throughout the video. This process resulted in half an hour of very dense tracking data. The behavior recognition dataset was generated by converting tracks into mini-scenes, a video subregion centered on each animal; each mini-scene was manually annotated with 12 distinct behavior types, resulting in over 20 hours of data. Benchmark results show mean average precision (mAP) of 92.62\% for the YOLOv8-X detection model, multiple object tracking precision (MOTA) of 63.81\% for the BotSort tracking algorithm, and micro top-1 accuracy of 63.97\% for the X3D behavior recognition model. Using deep learning to classify wildlife behavior from drone footage facilitates non-invasive insight into the collective behavior of an entire group.

Related papers

• POPCat: Propagation of particles for complex annotation tasks [7.236620861573004]
  We propose a time efficient method called POPCat that exploits the multi-target and temporal features of video data. The method generates a semi-supervised pipeline for segmentation or box-based video annotation. The method shows a margin of improvement on recall/mAP50/mAP over the best results.
  arXiv  Detail & Related papers  (2024-06-24T23:43:08Z)
• From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave [0.0]
  We introduce ChimpBehave, a novel dataset featuring over 2 hours of video (approximately 193,000 video frames) of zoo-housed chimpanzees. ChimpBehave meticulously annotated with bounding boxes and behavior labels for action recognition. We benchmark our dataset using a state-of-the-art CNN-based action recognition model.
  arXiv  Detail & Related papers  (2024-05-30T13:11:08Z)
• Parsing is All You Need for Accurate Gait Recognition in the Wild [51.206166843375364]
  This paper presents a novel gait representation, named Gait Parsing Sequence (GPS) GPSs are sequences of fine-grained human segmentation, extracted from video frames, so they have much higher information entropy. We also propose a novel human parsing-based gait recognition framework, named ParsingGait. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait.
  arXiv  Detail & Related papers  (2023-08-31T13:57:38Z)
• PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking [90.29143475328506]
  We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. We animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos.
  arXiv  Detail & Related papers  (2023-07-27T17:58:11Z)
• 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)
• Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data [7.667274758235099]
  We developed a pipeline to analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus) The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf)
  arXiv  Detail & Related papers  (2023-04-19T13:46:16Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• Anomaly Detection in Aerial Videos with Transformers [49.011385492802674]
  We create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos. There are 87,488 color video frames (51,635 for training and 35,853 for testing) with the size of $640 times 640$ at 30 frames per second. We present a new baseline model, ANomaly Detection with Transformers (ANDT), which treats consecutive video frames as a sequence of tubelets.
  arXiv  Detail & Related papers  (2022-09-25T21:24:18Z)
• 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)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild [51.35013619649463]
  We present an extensive dataset of free-running cheetahs in the wild, called AcinoSet. The dataset contains 119,490 frames of multi-view synchronized high-speed video footage, camera calibration files and 7,588 human-annotated frames. The resulting 3D trajectories, human-checked 3D ground truth, and an interactive tool to inspect the data is also provided.
  arXiv  Detail & Related papers  (2021-03-24T15:54:11Z)

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.