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REVIEW 3 major objections 5 minor 38 references

An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A simple detect-then-reset pipeline detects hands in egocentric video at 133 FPS with an F1-score of 0.87, close to the best detector alone and twice as fast.

desk verdict Solid engineering paper with a useful dataset, but the DAT hyperparameters may have been tuned on cross-validation test folds—fixable, but needs clarification. read the letter →

arxiv 1908.10406 v1 pith:3GJHB523 submitted 2019-08-27 cs.CV

Ryan J. Visée , Jirapat Likitlersuang , José Zariffa This is my paper · ORCID
classification cs.CV
keywords egocentricvideohanddetectionobjecttrackingdetector-assistedspinalcordinjuryrehabilitationYOLOv2KCFtracker
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes Detector-Assisted Tracking (DAT): a detector initializes and periodically resets an online hand tracker, so any given frame is processed by either detection or tracking, not both. The authors evaluate the approach on a new 167,622-frame egocentric dataset of 17 people with spinal cord injury performing daily living tasks in a home-like setting. The best configuration, YOLOv2 (You Only Look Once v2) combined with the KCF tracker, reset every 200 frames and engaged after 8 consistent detections, reaches an F1-score of 0.87 at 133 FPS on a GPU. That is close to the best detector alone (Single Shot Multibox, 0.90 at 44 FPS) and roughly twice as fast as the fastest detector alone (YOLOv2, 0.88 at 68 FPS), while online trackers by themselves score at most 0.42. The point is that reliable hand detection can be made fast enough for portable rehabilitation monitoring without losing much accuracy.

What carries the argument

The central mechanism is Detector-Assisted Tracking (DAT), a state machine that alternates between a detector (YOLOv2) and an online tracker (KCF, the Kernelized Correlation Filter, or Median Flow). The tracker is initialized only after the detector produces several consecutive detections with pairwise overlap above 0.1, which filters out sporadic false positives; it is reset at a fixed number of frames (reset iterations) or immediately on failure, and disabled when the hand is absent while the detector re-checks every few frames. This schedule is what lets the pipeline run at 133 FPS while keeping F1 within 0.01 of the best detector alone.

What would settle it

Run the YOLO_KCF 200/8/30 configuration on egocentric video in which a static skin-colored object (a face, a wooden table, a clay bowl) stays in view for many seconds; if the detector finds it in eight consecutive frames with overlap above 0.1, the tracker will initialize on it, and the F1 measure on the hand should fall well below the reported 0.87. A direct check on the ANS SCI test set is to compute F1 only on frames where the hand is absent but skin-toned background is visible, and see whether false positives rise.

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Extended reading notes

Core claim

The paper claims that, for a single hand in first-person video, the expensive per-frame detection step can be replaced by a cheaper tracker that is re-anchored by the detector on a fixed schedule and after failures. On the ANS SCI hand detection dataset, YOLO_KCF 200/8/30 achieves F1 0.87 ± 0.07 at 133 FPS on a GPU, compared with 0.88 ± 0.07 at 68 FPS for YOLOv2 alone and 0.90 ± 0.07 at 44 FPS for SSD. KCF alone scores 0.32 ± 0.18, so the combined accuracy is attributed to the detector's resets rather than to the tracker's standalone quality. On public datasets, the same configuration reaches 0.90 on EDSH and 0.58 on EgoHands, and its EgoHands precision for the camera-wearer's hands (0.722) exceeds the published baseline of 0.684.

Load-bearing premise

The pipeline assumes false positives will not be detected consistently across frames and that hands barely move between consecutive frames, so if a persistent skin-colored object triggers repeated detections or a hand moves quickly, the tracker can lock onto the wrong target and the reset logic may fail to recover.

Editorial extensions

If this is right

  • At 200/8/30, the YOLO_KCF combination reaches an F1 of 0.87 at 133 FPS on a GPU, roughly double the speed of YOLOv2 alone for a 0.01 F1 drop.
  • On a mid-range laptop CPU, the most accurate DAT configuration runs at 4.4 FPS versus 0.3 FPS for YOLOv2 alone, a 15-fold speedup that moves the method toward the 15-20 FPS target for portable rehabilitation devices.
  • DAT is twice as accurate as the best tracker alone (Median Flow, 0.42 F1), confirming that detector resets, not tracker quality, drive the gain.
  • The same configuration generalizes to EDSH (0.90 F1) and EgoHands (0.58 F1), indicating the method transfers outside the home-simulation training distribution.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the same alternation principle should apply to any single-object detection task in continuous wearable video, where detector calls can be spent only on appearance, failure, and scheduled re-anchoring events.
  • Editorial inference: a testable extension is adaptive scheduling—using the tracker's own confidence or objectness score to trigger resets instead of fixed iteration counts, which would likely shift the accuracy-speed frontier further.
  • Editorial inference: the EgoHands result suggests the method assumes the wearer's hand is the dominant object and few other hands appear; a version that models multiple hands or explicitly handles 'other hands' would be needed for social-interaction scenes.
  • Editorial inference: because DAT avoids per-frame detection, it reduces compute and could support on-device processing that never stores raw video, which would address privacy concerns raised in the paper.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes Detector-Assisted Tracking (DAT), a method that combines an object detector (YOLOv2) with an online tracker (KCF, Median Flow, MIL, or OLB) for hand detection in egocentric videos, targeting rehabilitation monitoring for individuals with spinal cord injury. The detector initializes and periodically resets the tracker, and the tracker is disabled when no hand is found for a number of consecutive frames. The method is evaluated on a newly labeled dataset of 167,622 frames from 17 participants with SCI, using participant-based three-fold cross-validation, and on two public datasets (EDSH and EgoHands). The best configuration, YOLO_KCF with reset iterations 200, consecutive IOU 8, and check iterations 30, achieves an F1-score of 0.87±0.07 at 133 FPS on a GPU, compared to 0.88±0.07 at 68 FPS for YOLOv2 alone. The paper concludes that DAT improves speed over detection-only methods and accuracy over tracking-only methods.

Significance. If the reported results are valid, the paper makes a useful practical contribution: a simple combination of existing components yields near-detector accuracy at substantially higher frame rates, which is relevant for deploying hand-function monitoring in home and community settings. The strengths include a relatively large, clinically relevant dataset; a participant-based cross-validation split accounting for impairment severity; comparison against three detectors and four trackers; and generalization tests on two public datasets. The work also explicitly acknowledges the assumption underlying the consecutive-IOU initialization rule. However, the headline numbers are threatened by a potential hyperparameter-selection leakage: the DAT hyperparameters were tuned on a subset of the same dataset used for cross-validation, and the manuscript does not state that the tuning participants were excluded from test folds. Since the F1 gap between the best DAT configuration and YOLOv2 alone is only 0.01, even modest selection bias could alter the central competitive-accuracy claim. The speed measurements also need clarification regarding the hardware used for the tracker in DAT.

major comments (3)
  1. [§IV.B / §III.D] The DAT hyperparameters (reset iterations, consecutive IOU, check iterations) were 'initially tested on the subset used by Visée et al. (19,683 frames spanning 6 participants and 4 environments)' and then the authors 'picked 3 models' for full-dataset evaluation. The cross-validation split in Section III.D is by participant into three groups, and the paper does not state that the six tuning participants were excluded from the test folds. If any of those participants appear in a test fold, the reported best configuration (YOLO_KCF 200/8/30, F1=0.87±0.07) was selected using information from the test set. Because the F1 difference between this configuration and YOLOv2 alone (0.88) is only 0.01, this leakage could alter the paper's central claim of competitive accuracy. The authors should either demonstrate that no tuning-subject frames were used for model selection, or re-run the evaluation with hyperparameters fixed a priori or via nested cross-validation.
  2. [§IV.A / §IV.B / Table IV] The speed comparison underlying the 'two times faster than the fastest detector alone' claim is not hardware-consistent as reported. Section IV.A states that online trackers were evaluated on CPU only ('Due to the efficiency of online trackers, evaluation was not performed on a GPU'), yet Table IV reports GPU FPS rates for YOLO_MF, YOLO_KCF, YOLO_MIL, and YOLO_OLB. The discussion further states that 'the MF and KCF trackers get a larger boost on a GPU,' which conflicts with the earlier statement. If the DAT pipeline uses YOLOv2 on the GPU and the tracker on the CPU, the 'GPU FPS' column is not a same-hardware comparison with YOLOv2 alone. The authors should specify exactly how each FPS number was measured, including which components ran on which processors, and adjust the comparison and wording accordingly.
  3. [§IV.C / Table V] The EgoHands comparison is not sufficiently rigorous to support the authors' generalization claim. The paper reports an average precision of 0.722 versus Bambach et al.'s 0.684, but it does not define how AP was computed for DAT on EgoHands, nor does it specify the protocol used to select the 100/9/60 configuration for that dataset. The text also notes that EgoHands contains 'other hands' in most frames while the evaluation excludes them; if Bambach et al.'s number is computed over all hands, the comparison is not apples-to-apples. Please provide the exact evaluation protocol, including how multiple detections per frame are matched, and either adopt an equivalent protocol or temper the claim to a qualitative illustration.
minor comments (5)
  1. [§IV.B] The reference to the feasibility study is cited as [23], which in the reference list is YOLO9000; it should likely be [35] (Visée et al., RESNA-Rehabweek 2019).
  2. [§III.D / §IV] The F1-score computation for frames with no ground-truth hand is not specified; the authors should state whether such frames are excluded or treated as true negatives, since this affects the reported precision and recall.
  3. [§III.C] The consecutive-IOU initialization assumption (that false positives will not persist across consecutive frames) is explicitly acknowledged, which is appropriate. A sensitivity analysis on sequences with persistent skin-colored distractors would help readers judge the robustness of the method in less controlled home environments.
  4. [Abstract / §IV.B] The abstract states the best combination is 'two times faster than the fastest detector alone'; the measured values (133 vs 68 FPS) correspond to 1.96 times faster, so 'approximately two times' would be more precise.
  5. [§IV.A / Table III] The tracker-alone results rely on manual initialization in the first 'good' frame chosen empirically; this should be stated more prominently as a limitation of the tracker-alone comparison, since it is not a realistic deployment scenario.

Circularity Check

1 steps flagged · score 4.0 of 10

DAT hyperparameters are tuned on a subset of the ANS SCI dataset that is included in the full cross-validation test folds, partially contaminating the headline F1; independent external benchmarks keep the central claim from being fully circular.

  1. fitted input called prediction [Section IV.B (DAT on ANS SCI), with the participant split described in Section III.D]
    "These parameters were initially tested on the subset used by Visée et al. (19,683 frames spanning 6 participants and 4 environments) [23]. ... Based on the results obtained from the subset of the ANS SCI dataset, we picked 3 models that resulted in the best trade-offs in F1-scores and FPS rates and evaluated them on the full ANS SCI dataset."

    The DAT hyperparameters (reset iterations, consecutive IOU, check iterations) were selected by F1/FPS ranking on a 19,683-frame subset of the ANS SCI dataset. The selected configurations were then evaluated on the full ANS SCI dataset, which contains that same subset by definition. Because the cross-validation split in Section III.D is by participant and every participant is in a test fold for one of the three folds, the 6 tuning participants' frames are necessarily part of the reported test-fold metrics. The reported F1=0.87 for YOLO_KCF 200/8/30 is therefore partly computed on the very frames used to choose that configuration, so this 'prediction' is not a fully independent estimate; it partially re-describes the selection data.

full rationale

This paper is an empirical systems study rather than a mathematical derivation, so most claimed results are direct measurements on held-out folds and public datasets. The main circularity-relevant step is the DAT hyperparameter selection in Section IV.B: the reset/IOU/check parameters were chosen using a 19,683-frame subset of the ANS SCI dataset and then the chosen models were evaluated on the full ANS SCI dataset. Since the full dataset includes that tuning subset, and the participant-based three-fold split places every participant in a test fold, the headline F1 for the best configuration is partially in-sample. This is a real but bounded leakage: the speed benefit is unaffected, the gain over trackers alone is large, and evaluation on EDSH and EgoHands is independent. The authors' citation of their own feasibility study [35] is a self-citation, but it is not load-bearing because the current paper re-runs the DAT evaluation on the full dataset rather than relying on [35]'s numbers. No derivation is forced by definition, so the paper is not fundamentally circular, but the hyperparameter tuning/test overlap prevents a clean score of 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central empirical claim depends on several domain assumptions rather than free parameters fitted to data. The DAT hyperparameters (reset iterations, consecutive IOU, check iterations) are free parameters selected on a subset, and the initialization logic rests on the stated assumption about false positive consistency. No new theoretical entities are introduced.

free parameters (4)
  • reset_iterations = 100, 200 (selected from subset)
    Number of frames between detector reinitializations; chosen on the 19,683-frame subset to balance F1 and speed, not fitted by a formal optimization.
  • consecutive_IOU = 3, 8, 9 (selected from subset)
    Number of consistent detector detections required before initializing the tracker; hand-picked based on subset experiments.
  • check_iterations = 30, 60 (selected from subset)
    Frequency of detector checks after the tracker is disabled; chosen empirically on the subset.
  • IOU_overlap_threshold = 0.1
    Overlap threshold used to decide if consecutive detections are consistent; stated as an assumption in Section III.C rather than fit to data.
assumptions (5)
  • domain assumption Manual bounding box annotations in the ANS SCI dataset are correct.
    All reported F1 scores depend on the quality of manual labels; annotation errors would directly bias the evaluation.
  • domain assumption False positives will not be detected consistently across consecutive frames.
    Central to the consecutive IOU initialization rule in Section III.C; if false positives persist, the tracker can lock onto them.
  • domain assumption Hands will not move considerably over consecutive frames, so a 0.1 overlap threshold is reasonable.
    Stated in Section III.C to justify the consecutive IOU overlap threshold; fast hand motion could violate this and prevent tracker initialization.
  • domain assumption IoU greater than 0.5 defines a correct detection, following PASCAL VOC.
    Used as the evaluation threshold; this is a community standard but still a choice that affects all F1 numbers.
  • domain assumption The UEMS-based participant split balances hand function difficulty across groups.
    The split assumes UEMS scores are a sufficient proxy for hand detection difficulty; ANOVA shows no mean difference, but residual variability in posture and environment is not controlled.

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Cite this review

Pith. "Pith review of An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications." pith.science (2026). https://pith.science/paper/3GJHB523

@misc{pith2026190810406,
  author       = {Pith},
  title        = {Pith review of: An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3GJHB523}},
  note         = {Machine review of arXiv:1908.10406}
}
abstract

Objective: Individuals with spinal cord injury (SCI) report upper limb function as their top recovery priority. To accurately represent the true impact of new interventions on patient function and independence, evaluation should occur in a natural setting. Wearable cameras can be used to monitor hand function at home, using computer vision to automatically analyze the resulting videos (egocentric video). A key step in this process, hand detection, is difficult to do robustly and reliably, hindering deployment of a complete monitoring system in the home and community. We propose an accurate and efficient hand detection method that uses a simple combination of existing detection and tracking algorithms. Methods: Detection, tracking, and combination methods were evaluated on a new hand detection dataset, consisting of 167,622 frames of egocentric videos collected on 17 individuals with SCI performing activities of daily living in a home simulation laboratory. Results: The F1-scores for the best detector and tracker alone (SSD and Median Flow) were 0.90$\pm$0.07 and 0.42$\pm$0.18, respectively. The best combination method, in which a detector was used to initialize and reset a tracker, resulted in an F1-score of 0.87$\pm$0.07 while being two times faster than the fastest detector alone. Conclusion: The combination of the fastest detector and best tracker improved the accuracy over online trackers while improving the speed of detectors. Significance: The method proposed here, in combination with wearable cameras, will help clinicians directly measure hand function in a patient's daily life at home, enabling independence after SCI.

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Reference graph

Works this paper leans on

38 extracted references · 37 canonical work pages

  1. [1]

    The economic burden of traumatic spinal cord injury in Canada,

    H. Krueger, V. K. Noonan, L. M. Trenaman, P. Joshi and C. S. Rivers, "The economic burden of traumatic spinal cord injury in Canada," Chronic diseases and injuries in Canada, vol. 33, 2013

    work page 2013

  2. [2]

    Targeting recovery: priorities of the spinal cord-injured population,

    K. D. Anderson, "Targeting recovery: priorities of the spinal cord-injured population," Journal of neurotrauma, vol. 21, pp. 1371-1383, 2004

    work page 2004

  3. [3]

    Learning to recognize objects in egocentric activities,

    A. Fathi, X. Ren and J. M. Rehg, "Learning to recognize objects in egocentric activities," in CVPR 2011, 2011

    work page 2011

  4. [4]

    Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions,

    S. Bambach, S. Lee, D. J. Crandall and C. Yu, "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions," in Computer Vision (ICCV), 2015 IEEE International Conference on, 2015

    work page 2015

  5. [5]

    Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community,

    J. Likitlersuang, E. R. Sumitro, P. Theventhiran, S. Kalsi-Ryan and J. Zariffa, "Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community," The journal of spinal cord medicine, vol. 40, pp. 706-714, 2017

    work page 2017

  6. [6]

    Egocentric video: a new tool for capturing hand use of individuals with spinal cord injury at home,

    J. Likitlersuang, E. R. Sumitro, T. Cao, R. J. Visée, S. Kalsi-Ryan and J. Zariffa, "Egocentric video: a new tool for capturing hand use of individuals with spinal cord injury at home," Journal of neuroengineering and rehabilitation, vol. 16, p. 83, 2019

    work page 2019

  7. [7]

    The graded redefined assessment of strength sensibility and prehension: reliability and validity,

    S. Kalsi-Ryan, D. Beaton, A. Curt, S. Duff, M. R. Popovic, C. Rudhe, M. G. Fehlings and M. C. Verrier, "The graded redefined assessment of strength sensibility and prehension: reliability and validity," Journal of neurotrauma, vol. 29, pp. 905-914, 2012

    work page 2012

  8. [8]

    A multicenter international study on the Spinal Cord Independence Measure, version III: Rasch psychometric validation,

    A. Catz, M. Itzkovich, L. Tesio, F. Biering-Sorensen, C. Weeks, M. T. Laramee, B. C. Craven, M. Tonack, S. L. Hitzig, E. Glaser and others, "A multicenter international study on the Spinal Cord Independence Measure, version III: Rasch psychometric validation," Spinal Cord, vol. 45, p. 275, 2007

    work page 2007

Show all 38 references
  1. [9]

    A review of wearable sensors and systems with application in rehabilitation,

    S. Patel, H. Park, P. Bonato, L. Chan and M. Rodgers, "A review of wearable sensors and systems with application in rehabilitation," Journal of neuroengineering and rehabilitation, vol. 9, p. 21, 2012

    2012

  2. [10]

    Wearable sensors for human activity monitoring: A review,

    S. C. Mukhopadhyay, "Wearable sensors for human activity monitoring: A review," IEEE sensors journal, vol. 15, pp. 1321-1330, 2015

    2015

  3. [11]

    Wearable Wireless Sensors for Rehabilitation,

    A. K. Dorsch, C. E. King and B. H. Dobkin, "Wearable Wireless Sensors for Rehabilitation," in Neurorehabilitation Technology, Springer, 2016, pp. 605-615

    2016

  4. [12]

    Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies,

    M. Noorkõiv, H. Rodgers and C. I. Price, "Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies," Journal of neuroengineering and rehabilitation, vol. 11, pp. 1- 11, 2014

    2014

  5. [13]

    Monitoring upper limb recovery after cervical spinal cord injury: insights beyond assessment scores,

    M. Brogioli, S. Schneider, W. L. Popp, U. Albisser, A. K. Brust, I.-M. Velstra, R. Gassert, A. Curt and M. L. Starkey, "Monitoring upper limb recovery after cervical spinal cord injury: insights beyond assessment scores," Frontiers in neurology, vol. 7, p. 142, 2016

    2016

  6. [14]

    Novel sensor technology to assess independence and limb-use laterality in cervical spinal cord injury,

    M. Brogioli, W. L. Popp, U. Albisser, A. K. Brust, A. Frotzler, R. Gassert, A. Curt and M. L. Starkey, "Novel sensor technology to assess independence and limb-use laterality in cervical spinal cord injury," Journal of neurotrauma, vol. 33, pp. 1950-1957, 2016

    1950

  7. [15]

    Design and evaluation of a low-cost instrumented glove for hand function assessment,

    N. P. Oess, J. Wanek and A. Curt, "Design and evaluation of a low-cost instrumented glove for hand function assessment," Journal of neuroengineering and rehabilitation, vol. 9, p. 2, 2012

    2012

  8. [16]

    The manumeter: a wearable device for monitoring daily use of the wrist and fingers,

    N. Friedman, J. B. Rowe, D. J. Reinkensmeyer and M. Bachman, "The manumeter: a wearable device for monitoring daily use of the wrist and fingers," IEEE journal of biomedical and health informatics, vol. 18, pp. 1804-1812, 2014

    2014

  9. [17]

    The use of a finger-worn accelerometer for monitoring of hand use in ambulatory settings,

    X. Liu, S. Rajan, N. Ramasarma, P. Bonato and S. I. Lee, "The use of a finger-worn accelerometer for monitoring of hand use in ambulatory settings," IEEE journal of biomedical and health informatics, vol. 23, pp. 599-606, 2018

    2018

  10. [18]

    Interaction Detection in Egocentric Video: Toward a Novel Outcome Measure for Upper Extremity Function,

    J. Likitlersuang and J. Zariffa, "Interaction Detection in Egocentric Video: Toward a Novel Outcome Measure for Upper Extremity Function," IEEE journal of biomedical and health informatics, vol. 22, pp. 561-569, 2016

    2016

  11. [19]

    A dynamic approach and a new dataset for hand- detection in first person vision,

    A. Betancourt, P. Morerio, E. I. Barakova, L. Marcenaro, M. Rauterberg and C. S. Regazzoni, "A dynamic approach and a new dataset for hand- detection in first person vision," in International conference on Computer Analysis of Images and Patterns, 2015

    2015

  12. [20]

    Rich feature hierarchies for accurate object detection and semantic segmentation,

    R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014

    2014

  13. [21]

    Faster r-cnn: Towards real-time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," in Advances in neural information processing systems, 2015

    2015

  14. [22]

    You only look once: Unified, real-time object detection,

    J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016

    2016

  15. [23]

    YOLO9000: Better, Faster, Stronger,

    J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," CoRR, vol. abs/1612.08242, 2016

    2016 arXiv

  16. [24]

    Ssd: Single shot multibox detector,

    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A. C. Berg, "Ssd: Single shot multibox detector," in European conference on computer vision, 2016

    2016

  17. [25]

    Real-time tracking via on-line boosting.,

    H. Grabner, M. Grabner and H. Bischof, "Real-time tracking via on-line boosting.," in Bmvc, 2006

    2006

  18. [26]

    Visual tracking with online multiple instance learning,

    B. Babenko, M.-H. Yang and S. Belongie, "Visual tracking with online multiple instance learning," in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 2009

    2009

  19. [27]

    Exploiting the circulant structure of tracking-by-detection with kernels,

    J. F. Henriques, R. Caseiro, P. Martins and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," in European conference on computer vision, 2012

    2012

  20. [28]

    Forward-backward error: Automatic detection of tracking failures,

    Z. Kalal, K. Mikolajczyk and J. Matas, "Forward-backward error: Automatic detection of tracking failures," in Pattern recognition (ICPR), 2010 20th international conference on, 2010

    2010

  21. [29]

    Learning to Track at 100 FPS with Deep Regression Networks,

    D. Held, S. Thrun and S. Savarese, "Learning to Track at 100 FPS with Deep Regression Networks," CoRR, vol. abs/1604.01802, 2016

    2016 arXiv

  22. [30]

    People-tracking-by-detection and people-detection-by-tracking,

    M. Andriluka, S. Roth and B. Schiele, "People-tracking-by-detection and people-detection-by-tracking," in 2008 IEEE Conference on computer vision and pattern recognition, 2008

    2008

  23. [31]

    A comparative view on exemplar ‘tracking-by-detection’approaches,

    E. Moussy, A. A. Mekonnen, G. Marion and F. Lerasle, "A comparative view on exemplar ‘tracking-by-detection’approaches," in 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2015

    2015

  24. [32]

    Detect to track and track to detect,

    C. Feichtenhofer, A. Pinz and A. Zisserman, "Detect to track and track to detect," in Proceedings of the IEEE International Conference on Computer Vision, 2017

    2017

  25. [33]

    F. Bu, Y. Cai and Y. Yang, Multiple Object Tracking Based on Faster- RCNN Detector and KCF Tracker, 2016

    2016

  26. [34]

    On-line boosting and vision,

    H. Grabner and H. Bischof, "On-line boosting and vision," in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006

    2006

  27. [35]

    Detecting hands in egocentric videos after spinal cord injury through a combination of object detection and tracking approaches,

    R. J. Visée, J. Likitlersuang and J. Zariffa, "Detecting hands in egocentric videos after spinal cord injury through a combination of object detection and tracking approaches," in RESNA - Rehabweek, Toronto, 2019

    2019

  28. [36]

    International standards for neurological classification of spinal cord injury (revised 2011),

    S. C. Kirshblum, S. P. Burns, F. Biering-Sorensen, W. Donovan, D. E. Graves, A. Jha, M. Johansen, L. Jones, A. Krassioukov, M. J. Mulcahey and others, "International standards for neurological classification of spinal cord injury (revised 2011)," The journal of spinal cord med...

    2011

  29. [37]

    Pixel-level hand detection in ego-centric videos,

    C. Li and K. M. Kitani, "Pixel-level hand detection in ego-centric videos," in Computer vision and pattern recognition (cvpr), 2013 ieee conference on, 2013

    2013

  30. [38]

    The Pascal Visual Object Classes (VOC) Challenge,

    M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, pp. 303-338, 6 2010

    2010

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