Annolid: Annotate, Segment, and Track Anything You Need
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Abstract (arXiv)
Annolid is a deep learning-based software package designed for the segmentation, labeling, and tracking of research targets within video files, focusing primarily on animal behavior analysis. Based on state-of-the-art instance segmentation methods, Annolid now harnesses the Cutie video object segmentation model to achieve resilient, markerless tracking of multiple animals from single annotated frames, even in environments in which they may be partially or entirely concealed by environmental features or by one another. Our integration of Segment Anything and Grounding-DINO strategies additionally enables the automatic masking and segmentation of recognizable animals and objects by text command, removing the need for manual annotation. Annolid's comprehensive approach to object segmentation flexibly accommodates a broad spectrum of behavior analysis applications, enabling the classification of diverse behavioral states such as freezing, digging, pup huddling, and social interactions in addition to the tracking of animals and their body parts.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 27 Mar 2024.
Builds on
17 of 29 references.
References · 17
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2023Segment Anythingcited by 8,234
- 2017Mask R-CNNcited by 7,277
- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
- 2023Segment Everything Everywhere All at Oncecited by 548
- 2022XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Modelcited by 439
- 2019Tracking without bells and whistlescited by 382
- 2023Segment Anything in High Qualitycited by 376
- 2023Faster Segment Anything: Towards Lightweight SAM for Mobile Applicationscited by 352
- 2023Fast Segment Anythingcited by 290
- 2023Tracking Anything with Decoupled Video Segmentationcited by 197
- 2023Putting the Object Back into Video Object Segmentationcited by 188
5 more
- 2023EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anythingcited by 188
- 2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
- 2022BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Videocited by 66
- 2023EdgeSAM: Prompt-In-the-Loop Distillation for SAMcited by 41
- 2023Automated Behavioral Analysis Using Instance Segmentationcited by 2
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Citing papers per year: 2024: 1.
1 paper.