Online tracking of ants based on deep association metrics: method, dataset and evaluation

Cao, Xiaoyan; Guo, Shihui; Lin, Juncong; Zhang, Wenshu; Liao, Minghong

doi:10.1016/j.patcog.2020.107233

Cited by 26 publications

(26 citation statements)

References 39 publications

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“…After downsampling eight times, the network will eventually obtain a 128-dimensional feature vector through a fully connected layer. The specific parameters are consistent with Cao et al (2020).…”

Section: Offline Resnet Network Architecturesupporting

confidence: 68%

“…We establish an video database of ant colony, which contains a total of 10 videos. Five videos are from an existing published work Cao et al (2020) and captured in the indoor (laboratory) environment. The remaining five outdoor videos are captured in different backgrounds and are obtained from the online website DepositPhotos (http://www.depositphotos.com).…”

Section: Ant Colony Databasementioning

confidence: 99%

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Swarm behavior tracking based on a deep vision algorithm

Wu¹,

Cao²,

Guo³

2022

Preprint

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The intelligent swarm behavior of social insects (such as ants) springs up in different environments, promising to provide insights for the study of embodied intelligence. Researching swarm behavior requires that researchers could accurately track each individual over time. Obviously, manually labeling individual insects in a video is labor-intensive. Automatic tracking methods, however, also poses serious challenges: (1) individuals are small and similar in appearance; (2) frequent interactions with each other cause severe and long-term occlusion. With the advances of artificial intelligence and computing vision technologies, we are hopeful to provide a tool to automate monitor multiple insects to address the above challenges. In this paper, we propose a detection and tracking framework for multi-ant tracking in the videos by: (1) adopting a two-stage object detection framework using ResNet-50 as backbone and coding the position of regions of interest to locate ants accurately; (2) using the ResNet model to develop the appearance descriptors of ants; (3) constructing long-term appearance sequences and combining them with motion information to achieve online tracking. To validate our method, we construct an ant database including 10 videos of ants from different indoor and outdoor scenes. We achieve a state-of-the-art performance of 95.7% mMOTA and 81.1% mMOTP in indoor videos, 81.8% mMOTA and 81.9% mMOTP in outdoor videos. Additionally, Our method runs 6-10 times faster than existing methods for insect tracking. Experimental results demonstrate that our method provides a powerful tool for accelerating the unraveling of the mechanisms underlying the swarm behavior of social insects.

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Section: Offline Resnet Network Architecturesupporting

confidence: 68%

Section: Ant Colony Databasementioning

confidence: 99%

Swarm behavior tracking based on a deep vision algorithm

Wu¹,

Cao²,

Guo³

2022

Preprint

Self Cite

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“…After downsampling eight times, the network will eventually obtain a 128-dimensional feature vector through a fully connected layer. The specific parameters are consistent with [28].…”

Section: Mot Frameworksupporting

confidence: 68%

“…We establish an video database of ant colony, which contains a total of 10 videos. Five videos are from an existing published work [28] Some video characteristics present challenges for detection and tracking algorithms, for example over-exposure for indoor videos and diverse background for outdoor ones.…”

Section: Ant Colony Databasementioning

confidence: 99%

Accurate detection and tracking of ants in indoor and outdoor environments

Cao

Guo

2020

Preprint

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Monitoring social insects' activity is critical for biologists researching their group mechanism. Manually labelling individual insects in a video is labour-intensive. Automated tracking social insects is particularly challenging: (1) individuals are small and similar in appearance; (2) frequent interactions with each other cause severe and long-term occlusion. We propose a detection and tracking framework for ants by: (1) adopting a two-stage object detection framework using ResNet-50 as backbone and coding the position of regions of interest to locate ants accurately; (2) using the ResNet model to develop the appearance descriptors of ants; (3) constructing long-term appearance sequences and combining them with motion information to achieve online tracking. To validate our method, we build a video database of ant colony captured in both indoor and outdoor scenes. We achieve a state-of-the-art performance of 95.7% mMOTA and 81.1% mMOTP in indoor videos, 81.8% mMOTA and 81.9% mMOTP in outdoor videos. Our method runs 6-10 times faster than existing methods for insect tracking. The datasets and code are made publicly available, we aim to contribute to an automated tracking tool for biologists in relevant domains.

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“…

Ecosystems are increasingly exposed to stressors, leading to unprecedented rates of biodiversity decline globally (Capdevila et al, 2022;Díaz et al, 2019). Our ability to reliably forecast ecosystems dynamics is limited by our capacity to understand what governs their composition, dynamics, function and structure (Dietze et al, 2018;Petchey et al, 2015).

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mentioning

confidence: 99%

Towards the fully automated monitoring of ecological communities

et al. 2022

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Ecosystems are increasingly exposed to stressors, leading to unprecedented rates of biodiversity decline globally (Capdevila et al., 2022;Díaz et al., 2019). Our ability to reliably forecast ecosystems dynamics is limited by our capacity to understand what governs their composition, dynamics, function and structure (Dietze et al., 2018;Petchey et al., 2015). To drive predictive ecology forward and design appropriate conservation strategies, we therefore need access to long-term, highresolution and standardised information about ecosystems' abiotic and biotic components (Farley et al., 2018;

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Online tracking of ants based on deep association metrics: method, dataset and evaluation

Cited by 26 publications

References 39 publications

Swarm behavior tracking based on a deep vision algorithm

Swarm behavior tracking based on a deep vision algorithm

Accurate detection and tracking of ants in indoor and outdoor environments

Towards the fully automated monitoring of ecological communities

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