The authors are grateful to the Dr. Joan B. Company that funded the publication of this work. Also, the authors are grateful to the technicians of the experimental aquarium zone at the Institute of Marine Sciences in Barcelona (ICM-CSIC) for their help during the experimental work.
This work was supported by the RITFIM project (CTM2010-16274; principal investigator: J. Aguzzi) founded by the Spanish Ministry of Science and Innovation (MICINN), and the TIN2015-66951-C2-2-R grant from the Spanish Ministry of Economy and Competitiveness.

The species used in this study is not an endangered or protected species. Sampling and laboratory experiments followed the Spanish legislation and internal institutional (ICM-CSIC) regulations regarding animal welfare. Animal sampling was conducted with the permission of the local authority (Regional Government of Catalonia).
1. Animal Maintenance and Sampling
NOTE: The following protocol is based on the assumption that researchers can sample N. n...

The authors have nothing to disclose.

The performance and representative results obtained with the video-tracking protocol confirmed its validity for applied research in the field of animal behavior, with a specific focus on social modulation and circadian rhythms of cohoused animals. The efficiency of animal detection (69%) and the accuracy of tag discrimination (89.5%) coupled with the behavioral characteristics (i.e., movement rate) of the target species used here suggest that this protocol is a perfect solution for long-term experimental trials (e.g., da...

In the last few years, automated image-based tracking has provided highly accurate datasets which can be used to explore basic questions in ecology and behavior disciplines1. These datasets can be used for the quantitative analysis of animal behavior2,3. However, each image methodology used for tracking animals and behavior evaluation has its strengths and limitations. In image-based tracking protocols that use spatial information from previous frames in a movie to track animals4,5,6, errors can be introduced when the paths of two animals cross. These errors are generally irreversible and propagate through time. Despite computational advances that reduce or almost eliminate this problem5,7, these techniques still need homogeneous experimental environments for accurate animal identification and tracking.
The employment of marks that can be uniquely identified in animals avoids these errors and allows the long-term tracking of identified individuals. Widely used markers (e.g.,&#160;barcodes and QR codes) exist in industry and commerce and can be identified using well-known computer vision techniques, such as augmented reality (e.g., ARTag8) and camera calibration (e.g.,&#160;CALTag9). Tagged animals have previously been used for high-throughput behavioral studies in different animal species, for example, ants3 or bees10, but some of these previous systems are not optimized for recognizing isolated tags3.
The tracking protocol presented in this paper is especially suitable for tracking animals in one-channel imagery, such as infrared (IR) light or monochromatic light (particularly, we use blue light). Therefore, the method developed does not use color cues, being also applicable to other settings where there are constraints in the illumination. In addition, we use customized tags designed so as not to disturb the lobsters and, at the same time, allow recording with low-cost cameras. Moreover, the method used here is based on frame-independent tag detection (i.e., the algorithm recognizes the presence of each tag in the image regardless of the previous trajectories). This feature is relevant in applications where animals can be temporarily occluded, or animals&#39; trajectories may intersect.
The tag design allows its use in different groups of animals. Once the parameters of the method are set, it could be transferred to tackle other animal-tracking problems without the need for training a specific classifier (other crustaceans or gastropods). The main limitations of exporting the protocol are the size of the tag and the need for attachment to the animal (which makes it not suitable for small insects, such as flies, bees, etc.) and the 2D assumption for the animal movement. This constraint is significant, given that the proposed method assumes the tag size remains constant. An animal moving freely in a 3D environment (e.g.,&#160;fish) would show different tag sizes depending on its distance to the camera.
The purpose of this protocol is to provide a user-friendly methodology for tracking multiple tagged animals over a long period of time (i.e., days or weeks) in a 2D context. The methodological approach is based on the use of open source software and hardware. Free and open source software permits adaptations, modifications, and free redistribution; therefore, the generated software improves at each step11,12.
The protocol presented here focuses on a laboratory set up to track and evaluate the locomotor activity of four aquatic animals in a tank for 5 days. The video files are recorded from a 1 s time-lapse image and compiled in a video at 20 frames per second (1 recorded day occupies approximately 1 h of video). All video recordings are automatically postprocessed to obtain animal positions, applying computer vision methods and algorithms. The protocol allows obtaining large amounts of tracking data, avoiding their manual annotation, which has been shown to be time-intensive and laborious in previous experimental papers13.
We use the Norway lobster (Nephrops norvegicus) for the case study; thus, we provide species-specific laboratory conditions to maintain them. Lobsters perform well-studied burrow emergence rhythms that are under the control of the circadian clock14,15, and when cohoused, they form dominance hierarchy16,17. Hence, the model presented here is a good example for researchers interested in the social modulation of behavior with a specific focus on circadian rhythms.
The methodology presented here is easily reproduced and can be applied to other species if there is a possibility to distinguish between animals with individual tags. The minimum requirements for reproducing such an approach in the laboratory are (i) isothermal rooms for the experimental setup; (ii) a continuous water supply; (iii) water temperature control mechanisms; (iv) a light control system; (v) a USB camera and a standard computer.
In this protocol, we use Python18 and OpenCV19 (Open Source Computer Vision Library). We rely on fast and commonly applied operations (both in terms of implementation and execution), such as background subtraction20 and image thresholding21,22.

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long-term video tracking of cohoused aquatic animals: a case study of the daily locomotor activity of the norway lobster (nephrops norvegicus)

We present a protocol related to a video-tracking technique based on the background subtraction and image thresholding that makes it possible to individually track cohoused animals. We tested the tracking routine with four cohoused Norway lobsters (Nephrops norvegicus) under light-darkness conditions for 5 days. The lobsters had been individually tagged. The experimental setup and the tracking techniques used are entirely based on the open source software. The comparison of the tracking output with a manual detection indicates that the lobsters were correctly detected 69% of the times. Among the correctly detected lobsters, their individual tags were correctly identified 89.5% of the times. Considering the frame rate used in the protocol and the movement rate of lobsters, the performance of the video tracking has a good quality, and the representative results support the validity of the protocol in producing valuable data for research needs (individual space occupancy or locomotor activity patterns). The protocol presented here can be easily customized and is, hence, transferable to other species where the individual tracking of specimens in a group can be valuable for answering research questions.

We manually constructed a subset of the experimental data to validate the automated video analysis. A sample size of 1,308 frames with a confidence level of 99% (which is a measure of security that shows whether the sample accurately reflects the population, within its margin of error) and a margin of error of 4% (which is a percentage that describes how close the response the sample gave is to the real value in the population) was randomly selected, and a manual annotation of the correct...

Watch this Scientific Journal Video about Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus at JoVE.com

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Here we present a protocol to individually track animals over a long period of time. It uses computer vision methods to identify a set of manually constructed tags by using a group of lobsters as case study, simultaneously providing information on how to house, manipulate, and mark the lobsters.

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

We present a protocol related to a video-tracking technique based on the background subtraction and image thresholding that makes it possible to ...

This protocol allows automation of the tracking of multiple animals. Using a simple label to identify each animal an ID analysis tool to detect the tags. This protocol significantly reduces the number of hours needed to analyze the video recordings using leskey data.to estimate more accurate completions from the experimental studies. Animals in other environments that can be tagged in similar fashion can be tracked with similar protocols providing application in chronobiology, ecology, viral research or neuroscience. We have provided the hardware design and open source programming language and provide a model we recommend users adopt in the protocols to learn Python programming language.To construct a tag for the target animal, first cut four 40 millimeter diameter circles from the black plastic sheet, and two 26 millimeter sided equilateral triangles and two 26 millimeter diameter circles from a white plastic PVC sheet. Mark the center of one white triangle and one white circle. And make a 10 millimeter hole at each mark.Then glue one white shape into the center of each of the four black circles. To set up the experimental area, place infrared dark time LEDs into the tank. Keep the infrared lights on at all times.Next, place blue light time LED lights into a modified fiberglass tank containing sand and four flexible PVC pipe burrows, and connect the lights to an apparatus for managing the photo period. When all of the lights have been placed, position the chilled sea water inlet at one corner of the tank, with the corresponding outlet at the opposite corner. Check that the sea water input is set up at a flow rate of about 4 liters per minute.Surround the chamber with a black curtain to provide full isolation from external light. Place the tripod equipped with the web camera to the side of the experimental arena, with the camera 130 centimeters above and at the center of the experimental arena. Connect the camera to a computer outside of the curtain, and adjust the parameters of the video recording according to the characteristics of the species, making sure to create a timestamp, including the date in the time-lapse video, for eventual behavioral scoring.To tag the animals, add 7 degree Celsius water to an icebox with submerged compartments, and place 4 lobster into 4 separated compartments. After allowing the lobsters to acclimate for about 30 minutes, transfer one lobster onto a tray of crushed ice for five minutes, and use absorptive paper to dry the upper part of the immobilized lobster's cephalothorax. Place a drop of fast drying glue on the dried carapace and press the tag onto the glue for about 20 seconds, until the glue hardens.Then return the lobster to its compartment in the icebox, and label the other three animals in the same manner. When all of the lobsters have been labeled, place the lobsters back into their cell for 24 hours. The next day, use the same icebox to transfer the lobsters from the acclimation facility to the experimental chamber, and initiate the video recording.Execute the tagging and transfer steps under the red light conditions to avoid radial damage to the lobster for the receptors. Additional water species or terrestrial species may not need this care. Then obtain an averaged background image from five minutes of video or from the initial 100 frames before introducing the animals one by one into the experimentation tank inside their respective compartments of water, and wait for the lobsters to come out of their compartments.Of the total number of animals counted in this representative experiment, 79 percent of the animal detections were correctly matched by the program, and 89.5 percent of the tags were correctly identified. Only 3.8 percent of tags were incorrectly classified. But the remaining 6.6 percent corresponding to false positives.After the completion of a specific video analysis, the obtained positions data can be used to evaluate the different behavioral patterns of the lobsters. For example, space occupancy map plotting, using a two-dimensional kernal density estimation with an axis aligned by variate normal kernal evaluated on a square grid, allows the areas in which the lobsters spend a higher percentage of their time to be represented with high color intensity plots. The daily activity rhythms of the lobsters can also be plotted as millimeters covered at ten minute binned intervals over time.It's important to maintain exact specifications in terms of the size and shape and to always execute the binned analysis step in the background with no user interface. The image analysis model can be expanded to look at specific components of the crustaceans to help to improve the behavioral study in terms of animal interactions. These complex animal social interactions occur in a similar fashion in an actual environment allowing the style and important aspects of biological research with some application for ecology and neuroscience.

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6.7K 次观看.  Universitat Oberta de Catalunya. 该协议允许对多个动物的跟踪自动化。使用简单的标签来识别每个动物的 ID 分析工具来检测标签。该协议显著减少了使用 leskey 数据分析视频录制所需的小时数。从实验研究中估计更准确的完成。其他环境中可以以类似方式标记的动物可以通过类似的协议进行跟踪，这些协议在时生生物学、生态学、病毒研究或神经科学中应用。我们提供了硬件设计和开源编程语言，并?...