SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We provide additional visualizations for four tracking methods introduced in Section 4.2 for the entire video sequence, as well as the ground truth annotation: GlobalTrack [34], LTMU [11], ToMP [55], STARK [84]. Due to size limit, we chose four example videos with hand and object interaction.
Computation. For finetuning STARK, we used one machine with 8 V100 GPUs, with a running time of roughly one day. For all other baselines, we only run inference, using 4 machines each with 8 V100 GPUs.
Annotation. Each video has an average of 3.8 objects, and each object track took between 1-2 hours to annotate the bounding boxes. The attributes annotation took around 30 seconds per object track. We estimate the total number of hours of annotation to be around 86k, including human annotations and quality check.
The bounding box annotation was done by human labelers. Although multiple rounds of reviews were used to ensure correctness and consistency, there still remains the potential for human errors or bias. In addition, the selection of objects is inherited from the Ego4D visual-query benchmark, which is biased towards objects with appearances of at least modest duration. Consequently, our benchmark may contain less instances that only appear very briefly in the video. However, the dataset does contain a moderate amount of occurrences for objects with short duration.
I Potential negative societal impacts
Since our videos are sourced from Ego4D, we inherit many of the potential negative impacts. Details are discussed in the original paper [29] Appendix K. We summarize several here:
- There may be risks surrounding privacy, such as personnel being recorded during the video. Consent was obtained in the original dataset, and a user agreement is enforced for Ego4D. Our dataset follows the same protocol as Ego4D.
- The existing efforts may inspire future data collection with less attention to privacy and ethics. Best practices were detailed in the original paper [29]. We did the same with our paper to include the instructions to help mitigate this risk.
- There may be data imbalances, such as geographical distribution. This risk can be mitigated with future work that grows the collaboration in underrepresented areas.
J Data sheet
We follow [25] for writing the data sheet of EgoTracks.
(a) For what purpose was the dataset created? (Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.)
Visual object tracking is key to many egocentric vision problems. However, the full spectrum of challenges of egocentric tracking faced by an embodied AI is underrepresented in many existing datasets, which tend to focus on short, third-person videos. Egocentric video has several distinguishing characteristics from those commonly found in past datasets: frequent large camera motions and hand interactions with objects commonly lead to occlusions or objects exiting the frame, and object appearance can change rapidly due to widely different points of view, scale, or object states. Embodied tracking is also naturally long-term, and being able to consistently (re-)associate objects to their appearances and disappearances over as long as a lifetime is critical. Previous datasets under-emphasize this re-detection problem, and their "framed" nature has led to adoption of various spatiotemporal priors that we find do not necessarily generalize to egocentric video. We thus introduce EgoTracks, a new dataset for long-term egocentric visual object tracking. Sourced from the Ego4D dataset, EgoTracks presents a significant challenge to recent state-of-the-art single-object trackers, which we find score more poorly on our new dataset than existing popular benchmarks, according to traditional tracking metrics.
(b) Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?
EgoTracks was created by Hao Tang, Kevin J Liang, Kristen Grauman, Matt Feiszli, and Weiyao Wang. All the authors were employed by Meta Platforms Inc. Kristen Grauman is also a professor at UT Austin.
(c) Who funded the creation of the dataset? (If there is an associated grant, please provide the name of the grantor and the grant name and number.)
Funding was provided by Meta Platforms Inc.
The code and dataset annotation is available for download from https://github.com/EG04D/episodic-memory/tree/main/EgoTracks.
(a) What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? (Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.)
Each instance is an object track from a video from the Ego4D dataset [29]. Object tracks are each represented as a series of bounding boxes across multiple frames.
(b) How many instances are there in total (of each type, if appropriate)?
There are 22028 instances in total. Please see Table 11 for details on how we split the dataset for training, validation, and test.
(c) Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? (If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).)
The dataset does not contain all possible instances in the videos. Instead, objects were selected according to those present in Ego4D's Visual Query benchmark, where object instances were chosen by annotators if they think the object is "interesting", which means the object that they think they might interact with or is important for an activity (see [29] for details). Since these object instances were inherited from Ego4D, we share the same human and geographic bias and limitations in object selection (see Appendix H).
(d) What data does each instance consist of? ("Raw" data (e.g., unprocessed text or images) or features? In either case, please provide a description.)
Each instance consists of 2D bounding boxes  $(x, y, w, d)$  for the object for each frame in the video where the object is visible. Also, for each time interval the object appears in the video, there are three binary labels: is.Active, is_transformed, and is_recognizable (see Section 3.3 of main paper for definitions).
(e) Is there a label or target associated with each instance? If so, please provide a description.
For each time interval the object appears in the video, there are three binary labels: is.Active, is_transformed and is recognizable. is.active means if the camera wearer is interacting with the object. is_transformed means if the object is undergoing any transformation (rigid or non-rigid) or state change. is recognizable means if the annotator can recognize the object given the time interval. Otherwise, the location specified by the bounding boxes comprises the label.
(f) Is any information missing from individual instances? (If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.)
No, we do not remove any information for individual instances already present in Ego4D [29].
(g) Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? (If so, please describe how these relationships are made explicit.)
No. There are on average roughly 3.86 tracks per track, and groups of videos in Ego4D [29] may have been captured by the same individual, but we do not expose this information in the dataset.
(h) Are there recommended data splits (e.g., training, development/validation, testing)? (If so, please provide a description of these splits, explaining the rationale behind them.) Yes, we follow the same data splits as [29]. Please refer to Table 11 for details.
(i) Are there any errors, sources of noise, or redundancies in the dataset? (If so, please provide a description.)
Since the bounding boxes are annotated by human raters, they are prone to human bias and errors. We applied quality assurance procedures to minimize such errors where we can.
(j) Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? (If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.)
The underlying videos are sourced from [29]. The [29] dataset is maintained by Ego4D consortium which can be regarded as guaranteed to exist and remain constant. The license can be found https://ego4d-data.org/pdfs/Ego4D-Licensees-Draft.pdf.
(k) Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals' non-public communications)? (If so, please provide a description.)
No. Ego4D [29] was collected with careful consideration of ethics and consent. We largely inherit these characteristics. The additional bounding box and attribute annotations of our dataset do not reveal any confidential information.
(1) Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? (If so, please describe why.)
We share the same underlying videos as [29], so we inherit its privacy and ethics standards, as well as any potential risks. The additional bounding box and attribute annotations of our dataset do not add any additional objectionable content.
(m) Does the dataset relate to people? (If not, you may skip the remaining questions in this section.)
Yes. The annotations we provide in EgoTracks do not contain any information relating to people; however, they are labeled from Ego4D [29] videos, which do contain people, and indirectly are related to people due to its egocentric nature.
(n) Does the dataset identify any subpopulations (e.g., by age, gender)? (If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.)
EgoTracks does not identify subpopulations, but the underlying dataset Ego4D [29] does. See Appendix C of [29] for more details.
(o) Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? (If so, please describe how.)
EgoTracks does not contain tracks of people, focusing instead on objects. Ego4D [29] does contain some visually identifiable individuals, who provided their consent to appear in the dataset. Other individuals have their faces blurred.
(p) Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? (If so, please provide a description.)
EgoTracks does not add any labels for people, focusing instead on objects. Ego4D [29] does contain demographics information. See (n) above.
(q) Any other comments? N/A.
(a) How was the data associated with each instance acquired? (Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.)
The bounding box for each instance was acquired by human annotators. They were instructed to draw a 2D bounding box for each frame the object appears in the video. To ensure data correctness and consistency, three independent reviewers are asked to annotate the same video. We find the overlaps between these independent annotations are high ( $>0.88$  IoU).
(b) What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? (How were these mechanisms or procedures validated?)
We use the proprietary annotation software from Meta to collect bounding box annotations. The software shows a video frame by frame, and the annotator is able to use mouse to draw a bounding box around the object. The software will then record the coordinate for each bounding box.
We used the entirety of Ego4D's Visual Queries benchmark [29] as the basis of EgoTracks.
The participants were contractors employed by a third-party vendor and are compensated based on the agreement with their employer.
(e) Over what timeframe was the data collected? (Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.)
The dataset was created in summer and fall of 2022, which is not the time the videos were collected.
(f) Were any ethical review processes conducted (e.g., by an institutional review board)? (If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.)
Yes. For proprietary reasons, we are not able to provide supporting documentation. Our internal review was conducted thoroughly vetted potential privacy and ethical related concerns.
(g) Does the dataset relate to people? (If not, you may skip the remaining questions in this section.)
Ego4D [29] collected the data from the individual directly. We do not collect any additional videos beyond those in Ego4D.
(i) Were the individuals in question notified about the data collection? (If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.)
Ego4D [29] was collected by willing and consenting individuals. See Section 3.4 in [29].
(j) Did the individuals in question consent to the collection and use of their data? (If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.)
(k) If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? (If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).) Yes, see Section 3.4 in [29].
(1) Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? (If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.)
4. Preprocessing/cleaning/labeling
(a) Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? (If so, please provide a description. If not, you may skip the remainder of the questions in this section.)
(b) Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? (If so, please provide a link or other access point to the "raw" data.)
(c) Is the software used to preprocess/clean/label the instances available? (If so, please provide a link or other access point.)
(a) Has the dataset been used for any tasks already? (If so, please provide a description.) The dataset has been used for understanding the performance of different single object tracking (SOT) trackers, and developing trackers that perform better in egocentric settings.
(b) Is there a repository that links to any or all papers or systems that use the dataset? (If so, please provide a link or other access point.)
We released our code for EgoSTARK, along with the dataset in https://github.com/EG04D/episodic-memory/tree/main/EgoTracks.
The dataset could possibly be used for acquiring segmentation masks, constructing 3D representation for the objects, or any other tasks that can use object tracks.
(d) Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? (For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?)
The selection of objects is inherited from the Ego4D visual-query benchmark, which is biased towards objects with appearances of at least modest duration. Consequently, our benchmark may contain less instances that only appear very briefly in the video. However, the dataset does contain a moderate amount of occurrences for objects with short duration.
(e) Are there tasks for which the dataset should not be used? (If so, please provide a description.)
We currently do not foresee anything such tasks.
(a) Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? (If so, please provide a description.)
Yes, we share the same license and distribution as [29].
(b) How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? (Does the dataset have a digital object identifier (DOI)?)
The dataset is distributed from GitHub https://github.com/EG04D/ episodic-memory/tree/main/EgoTracks.
It is currently available on GitHub.
(d) Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? (If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.) We share the same license as Ego4D. Please see https://ego4d-data.org/pdfs/Ego4D-Licensees-Draft.pdf.
(e) Have any third parties imposed IP-based or other restrictions on the data associated with the instances? (If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.)
Not to our knowledge.
(f) Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? (If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.)
All authors are maintaining the dataset. The Ego4D team is also supporting the dataset.
(b) How can the owner/curator/manager of the dataset be contacted (e.g., email address)?
Authors can be contacted via emails, or via the Ego4D forum https://discuss.ego4d-data.org/top?period=yearly.
(c) Is there an erratum? (If so, please provide a link or other access point.)
Not currently. Future versions of the dataset may be released if we find errors, which will be provided within the same GitHub.
(d) Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances'? (If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?)
(e) If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? (If so, please describe these limits and explain how they will be enforced.)
N/A. The dataset is not related to people.
(f) Will older versions of the dataset continue to be supported/hosted/maintained? (If so, please describe how. If not, please describe how its obsolescence will be communicated to users.)
Yes, all data will be versioned.
(g) If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? (If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to other users? If so, please provide a description.)
Errors/features can be submitted as issues/pull requests on GitHub.