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111 results for “obstacles”
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 11: The second obstacle
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 16: The third obstacle with 100 robots after passing some robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance.</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 10: First obstacle with 100 robots after passing all the robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 9: First obstacle with 100 robots before passing any robot
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 20: The fourth obstacle with 100 robots after passing some robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 8 : The first obstacle (allows one robot to pass at a time)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 18: Fourth Obstacle (a part of the obstacle can be passed by a group set but the other part can be passed by only one robot at a time)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 17: The third obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first<br> obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15,<br> 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to<br> present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle<br> and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 4: Methodology flowchart
<p>The detailed methodology approach is given in Figure 4:</p> <p>a) Generate a random number of robots to have a swarm.<br> b) Organize the robots in circular formations, where every robot will be in a group related to<br> circular formation. This means every circular formation is a cycle that has cyclic groups of robots<br> on its circumference as shown in Figure 2 and Figure 3. We might have more than one circular<br> formation.<br> c) Move the robots forward in a steady state.<br> d) Avoiding obstacles in case of facing an obstacle, and the swarm must adapt itself based on<br> the type of the obstacle. Various types of obstacles will be considered.<br> e) The swarm reorganizes itself after avoiding the obstacle in the same way as it was before<br> facing the obstacle. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 6: Swarm Robotics Move Forward
<p>The pseudo code description to calculate the Swarm robotics circular formation is given in Algorithm 3 and Figure 6. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 7. The first obstacle with 100 robots after passing some robots
<p>The pseudo code description to move the group set to avoid the predefined first obstacle as shown later in experimentation section. First obstacle crossing is given in Algorithm 5 and Figure 7. </p> <p>Algorithm 5: Move Swarm Robotics Over First Obstacles</p> <p>Purpose: Avoid First Obstacles one robot can pass through obstacles</p> <p>.Input: Constant W= Width Of Gap on Obstacles, Oc= Center of Gap begin of Obstacle Oc2= Center of Gap end of Obstacle {p1,p2,p3} position of three robots on circumference of group set circle GSN group set number</p> <p>{P1,…,Pn}set of position for robots on group set {r1,…,rn }∈GSN DRH Distance Swarm Robotics Will Stop in Before Obstacles Cxy' New Circular Formation Center After Avoid Obstacles Cg: =cx, cy center of group set </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 2: A robot set with ten groups of robots
<p>Methodology and the proposed model :</p> <p>a) Generate a random number of robots to have a swarm.</p> <p>b) Organize the robots in circular formations, where every robot will be in a group related to circular formation. This means every circular formation is a cycle that has cyclic groups of robots on its circumference as shown in Figure 2 and Figure 3. We might have more than one circular formation.</p> <p>c) Move the robots forward in a steady state.</p> <p>d) Avoiding obstacles in case of facing an obstacle, and the swarm must adapt itself based on the type of the obstacle. Various types of obstacles will be considered.</p> <p>e) The swarm reorganizes itself after avoiding the obstacle in the same way as it was before facing the obstacle. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 5: Graph Illustrate the new points on the circumference of the circle
<p>Formulas (1) and (2) are used to determine the new points on the circumference of the circle to form the circular formation shown in Figure 5. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 1: Circular formation for nine hundred robots
<p>The previous works and studies have talked about swam robotics motion in different formations like V and Fish school in addition to areas covering capability for searching and saving missions. Our proposed approach is an attempt to reduce the computational complexity of some of the previous approaches and to make robots motion more simple and capable of avoiding obstacles regardless of the obstacles structure. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 1b.The five steps of Methodology Approach
<p>The General diagram of the proposed approach is shown in Figure 1.</p> <p>A fundamental problem in collective robotics is to have the group organize into global formations or patterns. These include simple patterns like circles, lines, uniform distribution within a circle or square, etc. In the presence of a central controller, these tasks are trivial, but this is not the case in a distributed system. The main goal is to have self-autonomy robots, where each behaves independently from the others based its surrounding environment including other robots' behavior. Each robot might not be aware that it works within a group. Ducatelle et al.(Ducatelle, Di Caro, Pinciroli, Mondada, & Gambardella, 2011) proposed a collective behavior based on network routing, capable of guiding a robot from a source area to a target. Similarly to what happens in packet routing, the robots keep a table of the distance of other robots with respect to the target. A robot can then use the entries in the table and reach the target. </p>
Comparing the route-choice behavior of pedestrians around obstacles in a virtual experiment and a field study
<p>There is a great controversy about the application of virtual experiment in pedestrian routing behavior research. We conducted field observations and virtual experiment to study the route choice behavior of pedestrians. The route choice behavior around obstacles are compared qualitatively. The results shows that distance to the exit routes as well as the density around the exits show great influence on the pedestrians' route choice behavior while the speed of frontal pedestrians shows no obvious impact on the route choice. Pedestrians prefer to choose local closer exit or the exit with less occupants. The results of logistic regression show that the similar results can be obtained in virtual experiment and field observation qualitatively. This work can verify the validity of the virtual experiment for studying route choice behavior of pedestrians, which is of great importance for the application of virtual experiment.</p> <p>The files are trajectories of the recorded videos in the field observation.</p>
FIG. 2 in "Bursting through all obstacles and traversing all distances" (Plin., NH 8.35.86): spatial memory of snakes in light of ancient literary accounts
FIG. 2. — Bacchus and Vesuvius. A huge snake is slithering in front of an altar. Pompeii, from the Lararium of the House of the Centenary, 55-79 AD, Naples. National Archaeological Museum,inv.112286, after Kuivalainen (2021: 117, C17).
FIG. 1 in "Bursting through all obstacles and traversing all distances" (Plin., NH 8.35.86): spatial memory of snakes in light of ancient literary accounts
FIG. 1. — The Python's cave (antron). Leto escapes from Python with Apollo and Artemis in her arms. Lost Apulian red-figure neck amphora, earlier IV BC, drawing by J. H. W. Tischbein after Ogden (2013a: 39, fig. 3).
Estimated stand-off distance between ADS-B equipped aircraft and obstacles
<p><strong>Summary</strong>:</p> <p>Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.</p> <p> </p> <p><strong>Description:</strong></p> <p>For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.</p> <p>For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the <a href="https://doi.org/10.2514/1.D0091">foundational research</a> used to support the <a href="https://doi.org/10.1520/F3442_F3442M-20">ASTM F3442/F3442M-20</a> well clear criteria of 2000 feet laterally and 250 feet AGL vertically.</p> <p>The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/releases/tag/v1.3">v1.3 uncorrelated encounter models</a> and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”</p> <p>Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.</p> <p>The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.</p> <p>It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.</p> <p>For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands <a href="https://adds-faa.opendata.arcgis.com/datasets/4d8fa46181aa470d809776c57a8ab1f6_0/about">open dataset</a>. Aircraft type was identified as part of the <a href="https://github.com/Airspace-Encounter-Models/em-processing-opensky">em-processing-opensky</a> workflow.</p> <ul> <li><em>All</em>: No filter, all observations that satisfied encounter conditions</li> <li><em>nearRunway</em>: Aircraft within or at 2 nautical miles of a runway</li> <li><em>awayRunway</em>: Observations more than 2 nautical miles from a runway</li> <li><em>glider</em>: Observations when aircraft type is a glider</li> <li><em>fwme</em>: Observations when aircraft type is a fixed-wing multi-engine</li> <li><em>fwse</em>: Observations when aircraft type is a fixed-wing single engine</li> <li><em>rotorcraft</em>: Observations when aircraft type is a rotorcraft</li> </ul> <p><strong>License</strong></p> <p>This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).</p> <p>This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.</p> <p>MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.</p> <p>As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.</p> <p><strong>Distribution Statement</strong></p> <p>DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.</p> <p>© 2021 Massachusetts Institute of Technology.</p> <p>Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.</p> <p>This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.</p> <p>This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein.</p> <p><strong>Download and Format:</strong></p> <p>The dataset is provided as a single .zip archive with multiple directories. The directories indicate the aircraft track dataset and types of obstacles. Within each directory are csv files corresponding to different the filtering criteria. These files are the total counts of observations (histogram) where columns corresponding to lateral (range) distance between the simulated obstacle and aircraft and rows correspond to the relative vertical separation. The files centers_x_ft.csv and centers_y_ft.csv are the center of each bin.</p> <p>Simply download the .zip file and extract. The MD5 checksum of the .zip file prior to uploading to Zenodo was 03e5aed725a4cf393683730e05521ba5.</p> <p><strong>MIT Lincoln Laboratory LL Group and Division this dataset is associated with</strong>:</p> <ul> <li>Group 42 / Division 4</li> <li>Topic: collision avoidance</li> <li>R&D Area: Air Traffic Control</li> <li>R&D Group: Surveillance Systems</li> </ul>
Vision-Based Obstacle Detection on Rail Tracks
<p><strong>Acknowledgement and Disclaimers</strong></p> <p>These data are a product of a research activity conducted in the context of the <a href="http://rails-project.eu">RAILS (Roadmaps for AI integration in the raiL Sector) project</a>. RAILS has received funding from the Shift2Rail Joint Undertaking (JU) under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.</p> <p>The information and views set out in this description are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this dataset. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.</p> <p>This "dataset" has been created for scientific purposes only to study the potentials of Deep Learning (DL) approaches when used to analyse Video Data in order to detect possible obstacles on rail tracks and thus avoid collisions. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.</p> <p> </p> <p><strong>Objectives of the Study</strong></p> <p>RAILS defined some pilot case studies to develop Proofs-of-Concept (PoCs), which are conceived as benchmarks, with the aim of providing insight towards the definition of technology roadmaps that could support future research and/or the deployment of AI applications in the rail sector. In this context, the main objectives of the specific PoC "Vision-Based Obstacle Detection on Rail Tracks" were to investigate: i) solutions for the generation of synthetic data, suitable for the training of DL models; and ii) the potential of DL applications when it comes to detecting any kind of obstacles on rail tracks while exploiting video data from a single RGB camera.</p> <p> </p> <p><strong>A Brief Overview of the Approach</strong></p> <p>A multi-modular approach has been proposed to achieve the objectives mentioned above. The resulting architecture includes the following modules:</p> <ul> <li>The Rails Detection Module (RDM) detects rail tracks. The output of the RDM is used by the ODM and ADM.</li> <li>The Object Detection Module (ODM) detects obstacles whose type is known in advance.</li> <li>The Anomaly Detection Module (ADM) identifies any possible anomaly on rail tracks. These include obstacles whose type is not known in advance.</li> <li>The Obstacle Detection Module merges the outputs from the ODM and the ADM.</li> <li>The Distance Estimation Module estimates the distance of objects and anomalies from the train.</li> </ul> <p>The research was specifically oriented at implementing the RDM-ADM pipeline. Indeed, the object detection approaches that would be used to implement the ODM have been widely investigated by the research community, instead, to the best of our knowledge, limited work has been done in the rails field in the context of anomaly detection. The RDM has been realised by adopting a <em>Semantic Segmentation</em> approach based on <em>U-Net</em>; while, to develop the ADM, a <em>Vector-Quantized Variational Autoencoder</em> trained in <em>Unsupervised </em>mode was leveraged. Further details can be found in the RAILS <a href="http://rails-project.eu/downloads/deliverables/">"Deliverable D2.3: WP2 Report on experimentation, analysis, and discussion of results"</a>.</p> <p> </p> <p><strong>Steps to implement the RDM-ADM pipeline and description of shared Data</strong></p> <p>The following list reports all the steps that have been performed to implement the RDM-ADM pipeline; the words in bold-italic refer to the files that are shared within this dataset:</p> <ol> <li>A Railway Scenario was generated in MathWorks' <a href="https://www.mathworks.com/products/roadrunner.html">RoadRunner</a>.</li> <li>A video (<strong><em>FreeTrackVideo</em></strong>) was recorded by simulating an RGB camera mounted in front of the train; no obstacles on rail tracks were considered in this phase.</li> <li>2000 frames (<strong><em>FreeTrack2KFrames</em></strong>) were extracted from the aforementioned video. The video contains 4143 frames, however, only 2000 (each other frame starting from the first one) were taken into account due to training time and GPU RAM constraints.</li> <li>Only 10% of the 2000 frames were manually labelled (i.e., 200 frames, a frame every 10 frames) by exploiting LabelMe; these frames were then subdivided into training and validation sets (<strong><em>InitialLabelledSet</em></strong>).</li> <li>Hence, a Semi-Automatic labelling algorithm was developed by leveraging self-training and transfer learning. This algorithm made it possible to label all the FreeTrack2KFrames starting from the InitialLabelledSet. The resulting labels can be found in <strong><em>FreeTrack2KLabels</em></strong>.</li> <li>Data Augmentation was then performed in order to introduce some aleatory in the dataset. Because of the same time and RAM constraints mentioned above, the FreeTrack2KFrames set of data was reduced further: 1600 frames were selected among the aforementioned 2000 and then 5 transformations (Bright, Dark, Rain, Shadow, and Sun Flare) were applied to obtain the dataset (<em><strong>FreeTrack16TrainSet</strong>, <strong>FreeTrack16ValSet</strong>, <strong>FreeTrack16TestSet</strong></em>) that was used to train, validate, and test the RDM.</li> <li>Once the RDM was trained, the FreeTrackVideo was processed to obtain the masked frames that were then used to build the dataset(s) to train, validate, and test the ADM. The ADM was studied by considering two different datasets: the <em>Non-Anomaly Dataset</em> (<strong><em>NAD</em></strong>), which basically contains all the frames of the FreeTrackVideo once processed by the RDM; and the <em>Augmented Non-Anomaly Dataset (<strong>A-NAD</strong>)</em>, which contains 9000 frames, 1500 of which were extracted from the NAD, while the remaining 7500 were obtained by applying the same transformations mentioned above.</li> <li>Lastly, when both the RDM and the ADM were trained, the performances of the whole RDM-ADM pipeline were tested on the <strong><em>WithCarVideo</em></strong> which depicts the same scenario as the FreeTrackVideo but it also depicts a car laying on the rail tracks (i.e., an obstacle).</li> </ol>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.