NORPPA: NOvel Ringed seal re-identification by Pelage Pattern
Aggregation
- URL: http://arxiv.org/abs/2206.02498v2
- Date: Tue, 7 Jun 2022 11:20:26 GMT
- Title: NORPPA: NOvel Ringed seal re-identification by Pelage Pattern
Aggregation
- Authors: Ekaterina Nepovinnykh, Ilia Chelak, Tuomas Eerola, Heikki
K\"alvi\"ainen
- Abstract summary: We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification.
Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal monitoring and conservation.
- Score: 0.1310865248866973
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We propose a method for Saimaa ringed seal (Pusa hispida saimensis)
re-identification. Access to large image volumes through camera trapping and
crowdsourcing provides novel possibilities for animal monitoring and
conservation and calls for automatic methods for analysis, in particular, when
re-identifying individual animals from the images. The proposed method NOvel
Ringed seal re-identification by Pelage Pattern Aggregation (NORPPA) utilizes
the permanent and unique pelage pattern of Saimaa ringed seals and
content-based image retrieval techniques. First, the query image is
preprocessed, and each seal instance is segmented. Next, the seal's pelage
pattern is extracted using a U-net encoder-decoder based method. Then,
CNN-based affine invariant features are embedded and aggregated into Fisher
Vectors. Finally, the cosine distance between the Fisher Vectors is used to
find the best match from a database of known individuals. We perform extensive
experiments of various modifications of the method on a new challenging Saimaa
ringed seals re-identification dataset. The proposed method is shown to produce
the best re-identification accuracy on our dataset in comparisons with
alternative approaches.
Related papers
- Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training [51.87027943520492]
We present a novel paradigm Diffusion-ReID to efficiently augment and generate diverse images based on known identities.
Benefiting from our proposed paradigm, we first create a new large-scale person Re-ID dataset Diff-Person, which consists of over 777K images from 5,183 identities.
arXiv Detail & Related papers (2024-06-10T06:26:03Z) - Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM [62.85895749882285]
Marine Animal (MAS) involves segmenting animals within marine environments.
We propose a novel feature learning framework, named Dual-SAM for high-performance MAS.
Our proposed method achieves state-of-the-art performances on five widely-used MAS datasets.
arXiv Detail & Related papers (2024-04-07T15:34:40Z) - A Transformer Model for Boundary Detection in Continuous Sign Language [55.05986614979846]
The Transformer model is employed for both Isolated Sign Language Recognition and Continuous Sign Language Recognition.
The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched.
The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos.
arXiv Detail & Related papers (2024-02-22T17:25:01Z) - Combining feature aggregation and geometric similarity for
re-identification of patterned animals [0.2511811744954182]
Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time.
For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual.
In this paper, we address the re-identification by combining two types of pattern similarity metrics.
arXiv Detail & Related papers (2023-08-11T18:19:16Z) - CamoFormer: Masked Separable Attention for Camouflaged Object Detection [94.2870722866853]
We present a simple masked separable attention (MSA) for camouflaged object detection.
We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies.
We propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results.
arXiv Detail & Related papers (2022-12-10T10:03:27Z) - 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) - SealID: Saimaa ringed seal re-identification dataset [0.10555513406636087]
The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in the Lake Saimaa, Finland.
We make our Saimaa ringed seal image (SealID) dataset (N=57) publicly available for research purposes.
arXiv Detail & Related papers (2022-06-05T20:35:32Z) - People Tracking and Re-Identifying in Distributed Contexts: Extension of
PoseTReID [0.0]
In our previous paper, we introduced PoseTReID which is a generic framework for real-time 2D multi-person tracking.
In this paper, we introduce a further study of PoseTReID framework in order to give a more complete comprehension of the framework.
arXiv Detail & Related papers (2022-05-20T11:06:58Z) - EDEN: Deep Feature Distribution Pooling for Saimaa Ringed Seals Pattern
Matching [0.17999333451993946]
pelage pattern matching is considered to solve the individual re-identification of the Saimaa ringed seals.
We propose a novel feature pooling approach that allow aggregating the local pattern features to get a fixed size embedding vector.
arXiv Detail & Related papers (2021-05-28T16:59:39Z) - Camera-aware Proxies for Unsupervised Person Re-Identification [60.26031011794513]
This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations.
We propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera.
Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model.
arXiv Detail & Related papers (2020-12-19T12:37:04Z) - Automatic Detection and Recognition of Individuals in Patterned Species [4.163860911052052]
We develop a framework for automatic detection and recognition of individuals in different patterned species.
We use the recently proposed Faster-RCNN object detection framework to efficiently detect animals in images.
We evaluate our recognition system on zebra and jaguar images to show generalization to other patterned species.
arXiv Detail & Related papers (2020-05-06T15:29:21Z)
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.