17.1.3.5.3 Cyclic Motion, Periodic Motion, Symmetric, for Walking and Gait Recognition

Tsai, P.S.[Ping-Sing], Shah, M.[Mubarak], Keiter, K.[Katharine], Kasparis, T.[Takis],
Cyclic Motion Detection for Motion Based Recognition,
PR(27), No. 12, December 1994, pp. 1591-1603.
Elsevier DOI Cyclic Motion. Of walking person. BibRef 9412

Cutler, R.[Ross], Davis, L.S.[Larry S.],
Robust Real-Time Periodic Motion Detection, Analysis, and Applications,
PAMI(22), No. 8, August 2000, pp. 781-796.
IEEE DOI 0010
BibRef
Earlier:
Robust Periodic Motion and Motion Symmetry Detection,
CVPR00(II: 615-622).
IEEE DOI 0005
BibRef
Earlier:
Real-Time Periodic Motion Detection, Analysis, and Applications,
CVPR99(II: 326-332).
IEEE DOI BibRef
Earlier:
View-Based Detection and Analysis of Periodic Motion,
DARPA98(237-242). BibRef
And: A2, A1: ICPR98(Vol I: 495-500).
IEEE DOI 9808
Track objects, self-similarity measure for periodic motion is also periodic. Mostly human motion -- walking.
See also Backpack: Detection of People Carrying Objects Using Silhouettes. BibRef

Ran, Y.[Yang], Weiss, I.[Isaac], Zheng, Q.F.[Qin-Fen], Davis, L.S.[Larry S.],
Pedestrian Detection via Periodic Motion Analysis,
IJCV(71), No. 2, February 2007, pp. 143-160.
Springer DOI 0609
BibRef
Earlier:
An Efficient and Robust Human Classification Algorithm using Finite Frequencies Probing,
OTCBVS04(132).
IEEE DOI 0502
Detect based on gait. Color and IR.
See also Real-Time Human Detection, Tracking, and Verification in Uncontrolled Camera Motion Environments. BibRef

Ran, Y.[Yang], Zheng, Q.F.[Qin-Fen], Weiss, I., Davis, L.S., Abd-Almageed, W., Zhao, L.[Liang],
Pedestrian Classification from Moving Platforms Using Cyclic Motion Pattern,
ICIP05(II: 854-857).
IEEE DOI 0512
BibRef

Xu, D.L.[Dao-Lin], Li, Z.G.[Zhi-Gang], Bishop, S.R.[Steven R.], Galvanetto, U.[Ugo],
Estimation of periodic-like motions of chaotic evolutions using detected unstable periodic patterns,
PRL(23), No. 1-3, January 2002, pp. 245-252.
Elsevier DOI 0201
BibRef

Magee, D.R.[Derek R.], Boyle, R.D.[Roger D.],
Detecting lameness using 'Re-sampling Condensation' and 'multi-stream cyclic hidden Markov models',
IVC(20), No. 8, June 2002, pp. 581-594.
Elsevier DOI 0206
BibRef
Earlier:
Detecting Lameness in Livestock using Resampling Condensation and Multi-stream Cyclic Hidden Markov Models,
BMVC00(xx-yy).
PDF File. 0009
BibRef

Boyd, J.E.[Jeffrey E.],
Synchronization of oscillations for machine perception of gaits,
CVIU(96), No. 1, October 2004, pp. 35-59.
Elsevier DOI 0409
BibRef
Earlier:
Video Phase-Locked Loops in Gait Recognition,
ICCV01(I: 696-703).
IEEE DOI 0106
Synchronize model with changes in image. BibRef

Chang, C.[Cheng], Ansari, R., Khokhar, A.A.,
Efficient tracking of cyclic human motion by component motion,
SPLetters(11), No. 12, December 2004, pp. 941-944.
IEEE Abstract. 0412
BibRef
Earlier:
Cyclic articulated human motion tracking by sequential ancestral simulation,
CVPR04(II: 45-52).
IEEE DOI 0408
BibRef
Earlier:
Robust tracking of cyclic nonrigid motion,
ICIP03(III: 337-340).
IEEE DOI 0312
BibRef

Ormoneit, D.[Dirk], Black, M.J.[Michael J.], Hastie, T.[Trevor], Kjellström, H.[Hedvig],
Representing cyclic human motion using functional analysis,
IVC(23), No. 14, 12 December 2005, pp. 1264-1276.
Elsevier DOI
PDF File. 0601
BibRef

Grahn, J., Kjellstrom, H.,
Using SVM for Efficient Detection of Human Motion,
PETS05(231-238).
IEEE DOI 0602
BibRef

Branzan Albu, A.[Alexandra], Yazdi, M.[Mehran], Bergevin, R.[Robert],
Detection of cyclic human activities based on the morphological analysis of the inter-frame similarity matrix,
RealTimeImg(11), No. 3, June 2005, pp. 219-232.
Elsevier DOI 0508
BibRef
Earlier: A2, A1, A3:
Morphological analysis of spatio-temporal patterns for the segmentation of cyclic human activities,
ICPR04(IV: 240-243).
IEEE DOI 0409
BibRef

Branzan Albu, A.[Alexandra], Bergevin, R., Quirion, S.,
Generic temporal segmentation of cyclic human motion,
PR(41), No. 1, January 2008, pp. 6-21.
Elsevier DOI 0710
Human motion analysis; Periodicity analysis; Temporal segmentation BibRef

Jean, F.[Frederic], Bergevin, R.[Robert], Branzan Albu, A.[Alexandra],
Computing and evaluating view-normalized body part trajectories,
IVC(27), No. 9, 3 August 2009, pp. 1272-1284.
Elsevier DOI 0906
BibRef
Earlier:
Trajectories normalization for viewpoint invariant gait recognition,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
Computing View-normalized Body Parts Trajectories,
CRV07(89-96).
IEEE DOI 0705
BibRef
Earlier:
Body Tracking in HumanWalk from Monocular Video Sequences,
CRV05(144-151).
IEEE DOI 0505
Body parts trajectories; View-invariance; Normalization; Gait BibRef

Jean, F.[Frederic], Branzan Albu, A.[Alexandra], Bergevin, R.[Robert],
Towards view-invariant gait modeling: Computing view-normalized body part trajectories,
PR(42), No. 11, November 2009, pp. 2936-2949.
Elsevier DOI 0907
Gait; View-invariance; Normalization; Body part trajectories BibRef

Jean, F.[Frederic], Bergevin, R.[Robert], Branzan Albu, A.[Alexandra],
Human gait characteristics from unconstrained walks and viewpoints,
VS11(1883-1888).
IEEE DOI 1201
BibRef

Havasi, L.[László], Szlávik, Z.[Zoltán], Szirányi, T.[Tamás],
Higher order symmetry for non-linear classification of human walk detection,
PRL(27), No. 7, May 2006, pp. 822-829.
Elsevier DOI 0604
BibRef
Earlier:
Eigenwalks: Walk Detection and Biometrics from Symmetry Patterns,
ICIP05(III: 289-292).
IEEE DOI 0512
Simplified symmetry; Pedestrian detection; Tracking; Surveillance; Kernel Fisher discriminant analysis; Gait analysis BibRef

Havasi, L.[László], Szlávik, Z.[Zoltán], Szirányi, T.[Tamás],
Detection of Gait Characteristics for Scene Registration in Video Surveillance System,
IP(16), No. 2, February 2007, pp. 503-510.
IEEE DOI 0702
BibRef

Meng, Q.G.[Qing-Gang], Li, B.H.[Bai-Hua], Holstein, H.[Horst],
Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach,
IVC(24), No. 8, August 2006, pp. 795-809.
Elsevier DOI 0608
Human periodic motion classification; Motion-based recognition; Moving light displays (MLDs); Motion power spectral analysis BibRef

Li, B.H.[Bai-Hua], Holstein, H.,
Recognition of human periodic motion: A frequency domain approach,
ICPR02(I: 311-314).
IEEE DOI 0211
BibRef

Li, B.H.[Bai-Hua], Meng, Q.G.[Qing-Gang], Holstein, H.[Horst],
Articulated motion reconstruction from feature points,
PR(41), No. 1, January 2008, pp. 418-431.
Elsevier DOI 0710
Non-rigid articulated motion; Point pattern matching; Non-rigid pose estimation; Motion tracking and object recognition BibRef

Li, B.H.[Bai-Hua], Meng, Q.G.[Qing-Gang], Holstein, H.[Horst],
Reconstruction of segmentally articulated structure in freeform movement with low density feature points,
IVC(22), No. 10, 1 September 2004, pp. 749-759.
Elsevier DOI 0409
Model based point feature matching. BibRef

Briassouli, A.[Alexia], Ahuja, N.[Narendra],
Extraction and Analysis of Multiple Periodic Motions in Video Sequences,
PAMI(29), No. 7, July 2007, pp. 1244-1261.
IEEE DOI 0706
BibRef
Earlier:
Estimation of Multiple Periodic Motions from Video,
ECCV06(I: 147-159).
Springer DOI 0608
Periodic motion. Applied to human and animal motions.
See also Combination of Accumulated Motion and Color Segmentation for Human Activity Analysis. BibRef

Briassouli, A., Ahuja, N.,
Integration of Frequency and Space for Multiple Motion Estimation and Shape-Independent Object Segmentation,
CirSysVideo(18), No. 5, May 2008, pp. 657-669.
IEEE DOI 0711
BibRef
Earlier:
Spatial and Fourier Error Minimization for Motion Estimation and Segmentation,
ICPR06(I: 94-97).
IEEE DOI 0609
BibRef
Earlier:
Integrated Spatial and Frequency Domain 2D Motion Segmentation and Estimation,
ICCV05(I: 244-250).
IEEE DOI 0510
BibRef
Earlier:
Fusion of frequency and spatial domain information for motion analysis,
ICPR04(II: 175-178).
IEEE DOI 0409
BibRef
And: A2, A1:
Joint Spatial and Frequency Domain Motion Analysis,
FGR06(197-204).
IEEE DOI 0604
BibRef

Azy, O.[Ousman], Ahuja, N.[Narendra],
Segmentation of periodically moving objects,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Mahanta, A.K.[Anjana Kakoti], Mazarbhuiya, F.A.[Fokrul Alom], Baruah, H.K.[Hemanta K.],
Finding calendar-based periodic patterns,
PRL(29), No. 9, 1 July 2008, pp. 1274-1284.
Elsevier DOI 0711
Frequent item sets; Locally frequent sets; Set superimposition; Partially periodic patterns; Superimposed intervals BibRef

Leung, V., Colombo, A.[Alberto], Orwell, J.[James], Velastin, S.A.[Sergio A.],
Modelling periodic scene elements for visual surveillance,
IET-CV(2), No. 2, June 2008, pp. 88-98.
DOI Link 0905
BibRef

Ribnick, E.[Evan], Papanikolopoulos, N.[Nikolaos],
3D Reconstruction of Periodic Motion from a Single View,
IJCV(90), No. 1, October 2010, pp. xx-yy.
Springer DOI 1007

See also Estimating 3D Positions and Velocities of Projectiles from Monocular Views. BibRef

Chen, T.P.[Trista P.], Chen, C.W.[Ching-Wei], Popp, P.[Phillip], Coover, B.[Bob],
Visual Rhythm Detection and Its Applications in Interactive Multimedia,
MultMedMag(18), No. 1, January-March 2011, pp. 88-95.
IEEE DOI 1103
BibRef

Junejo, I.N.[Imran N.], Dexter, E.[Emilie], Laptev, I.[Ivan], Pérez, P.[Patrick],
View-Independent Action Recognition from Temporal Self-Similarities,
PAMI(33), No. 1, January 2011, pp. 172-185.
IEEE DOI 1011
BibRef
Earlier:
Cross-View Action Recognition from Temporal Self-similarities,
ECCV08(II: 293-306).
Springer DOI 0810
Self-similarities of actions sequences over time. Descriptor captures temporal similarities within a sequence. BibRef

Dexter, E.[Emilie], Pérez, P.[Patrick], Laptev, I.[Ivan],
Multi-view synchronization of human actions and dynamic scenes,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Laptev, I.[Ivan], Belongie, S.J.[Serge J.], Pérez, P.[Patrick], Wills, J.[Josh],
Periodic Motion Detection and Segmentation via Approximate Sequence Alignment,
ICCV05(I: 816-823).
IEEE DOI 0510
BibRef

Belongie, S.J.[Serge J.], Wills, J.[Josh],
Structure from Periodic Motion,
SCVMA04(16-24).
Springer DOI 0405
BibRef

Zhang, Z.J.[Zhi-Jun], Zhang, Y.[Yunong],
Acceleration-Level Cyclic-Motion Generation of Constrained Redundant Robots Tracking Different Paths,
SMC-B(42), No. 4, August 2012, pp. 1257-1269.
IEEE DOI 1208
BibRef

Yogameena, B., Roomi, S.M.M.[S. Md. Mansoor], Jyothi, R.R., Raju, P.S.[Priya S.], Abhaikumar, V.V.,
People/vehicle classification by recurrent motion of skeleton features,
IET-CV(6), No. 5, 2012, pp. 442-450.
DOI Link 1210
BibRef

Mansur, A., Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
Inverse Dynamics for Action Recognition,
Cyber(43), No. 4, 2013, pp. 1226-1236.
IEEE DOI 1307
Biological system modeling BibRef

Mansur, A.[Al], Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
View-invariant gait recognition from low frame-rate videos,
ICPR12(2383-2386).
WWW Link. 1302
BibRef

Akae, N.[Naoki], Mansur, A.[Al], Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
Video from nearly still: An application to low frame-rate gait recognition,
CVPR12(1537-1543).
IEEE DOI 1208
BibRef

Mori, A.[Atsushi], Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
Gait Recognition Using Period-Based Phase Synchronization for Low Frame-Rate Videos,
ICPR10(2194-2197).
IEEE DOI 1008
BibRef

Lee, C.P.[Chin Poo], Tan, A.W.C.[Alan W.C.], Tan, S.C.A.[Shing Chi-Ang],
Gait probability image: An information-theoretic model of gait representation,
JVCIR(25), No. 6, 2014, pp. 1489-1492.
Elsevier DOI 1407
Gait BibRef

Azhar, F., Tjahjadi, T.[Tardi],
Significant Body Point Labeling and Tracking,
Cyber(44), No. 9, September 2014, pp. 1673-1685.
IEEE DOI 1410
image motion analysis BibRef

Rida, I.[Imad], Almaadeed, S.[Somaya], Bouridane, A.[Ahmed],
Gait Recognition Based on Modified Phase-Only Correlation,
SIViP(10), No. 3, March 2016, pp. 463-470.
WWW Link. 1602

See also Automatic Recognition of Partial Shoeprints Based on Phase-Only Correlation. BibRef

Rida, I.[Imad], Bouridane, A.[Ahmed], Al Kork, S.[Samer], Bremond, F.[François],
Gait Recognition Based on Modified Phase Only Correlation,
ICISP14(417-424).
Springer DOI 1406

See also Automatic Recognition of Partial Shoeprints Based on Phase-Only Correlation. BibRef

Li, J.[Jun], Wang, J.J.[Jing-Jing], Zhang, J.F.[Jun-Fei], Qin, Q.M.[Qi-Ming], Jindal, T.[Tanvi], Han, J.W.[Jia-Wei],
A probabilistic approach to detect mixed periodic patterns from moving object data,
GeoInfo(20), No. 4, October 2016, pp. 715-739.
Springer DOI 1610
Multiple periods, not a single motion. BibRef

Runia, T.F.H.[Tom F. H.], Snoek, C.G.M.[Cees G. M.], Smeulders, A.W.M.[Arnold W. M.],
Repetition Estimation,
IJCV(127), No. 9, September 2019, pp. 1361-1383.
Springer DOI 1908
In video. Repeating actions. BibRef

Ye, M., Yang, C., Stankovic, V., Stankovic, L., Cheng, S.,
Distinct Feature Extraction for Video-Based Gait Phase Classification,
MultMed(22), No. 5, May 2020, pp. 1113-1125.
IEEE DOI 2005
Feature extraction, Legged locomotion, Kinematics, Tracking, Trajectory, Training, Foot, Feature extraction, gait phase classification BibRef

Stergiou, A.[Alexandros], Poppe, R.[Ronald],
Learn to cycle: Time-consistent feature discovery for action recognition,
PRL(141), 2021, pp. 1-7.
Elsevier DOI 2101
Squeeze and recursion, Temporal gates, Temporal cyclic error, 3D-CNNs, Spatio-temporal CNNs, Action recognition BibRef

Xu, C.[Chi], Makihara, Y.S.[Yasu-Shi], Li, X.[Xiang], Yagi, Y.S.[Yasu-Shi], Lu, J.F.[Jian-Feng],
Cross-View Gait Recognition Using Pairwise Spatial Transformer Networks,
CirSysVideo(31), No. 1, January 2021, pp. 260-274.
IEEE DOI 2101
BibRef
Earlier:
Gait Recognition from a Single Image Using a Phase-aware Gait Cycle Reconstruction Network,
ECCV20(XIX:386-403).
Springer DOI 2011
Gait recognition, Task analysis, Transforms, Probes, Measurement, Support vector machines, Databases, Pairwise spatial transformer, cross-view BibRef

Xu, C.[Chi], Makihara, Y.S.[Yasu-Shi], Li, X.[Xiang], Yagi, Y.S.[Yasu-Shi], Lu, J.F.[Jian-Feng],
Speed Invariance vs. Stability: Cross-Speed Gait Recognition Using Single-Support Gait Energy Image,
ACCV16(II: 52-67).
Springer DOI 1704
BibRef

Yin, J.Q.[Jian-Qin], Wu, Y.C.[Yan-Chun], Zhu, C.R.[Chao-Ran], Yin, Z.J.[Zi-Jin], Liu, H.P.[Hua-Ping], Dang, Y.H.[Yong-Hao], Liu, Z.Y.[Zhi-Yi], Liu, J.[Jun],
Energy-Based Periodicity Mining With Deep Features for Action Repetition Counting in Unconstrained Videos,
CirSysVideo(31), No. 12, December 2021, pp. 4812-4825.
IEEE DOI 2112
Videos, Feature extraction, Principal component analysis, Motion segmentation, Market research, Task analysis, deep ConvNets BibRef

Liu, X.L.[Xiao-Li], Yin, J.Q.[Jian-Qin], Guo, D.[Di], Liu, H.P.[Hua-Ping],
Rich Action-Semantic Consistent Knowledge for Early Action Prediction,
IP(33), 2024, pp. 479-492.
IEEE DOI Code:
WWW Link. 2401
Videos, Semantics, Predictive models, Task analysis, Pipelines, Feature extraction, Visualization, Action prediction, graph neural network BibRef

Jacquelin, N.[Nicolas], Vuillemot, R.[Romain], Duffner, S.[Stefan],
Periodicity counting in videos with unsupervised learning of cyclic embeddings,
PRL(161), 2022, pp. 59-66.
Elsevier DOI 2209
Unsupervised learning, Periodicity, Repetition, Embedding, Triplet loss BibRef

Sheng, X.X.[Xiao-Xiao], Li, K.[Kunchang], Shen, Z.Q.[Zhi-Qiang], Xiao, G.[Gang],
A Progressive Difference Method for Capturing Visual Tempos on Action Recognition,
CirSysVideo(33), No. 3, March 2023, pp. 977-987.
IEEE DOI 2303
Visualization, Videos, Spatiotemporal phenomena, Computational modeling, Motion segmentation, Semantics, progressive difference BibRef

Moniruzzaman, M., Yin, Z.Z.[Zhao-Zheng], He, Z.H.[Zhi-Hai], Leu, M.C.[Ming C.], Qin, R.[Ruwen],
Jointly-Learnt Networks for Future Action Anticipation via Self-Knowledge Distillation and Cycle Consistency,
CirSysVideo(33), No. 7, July 2023, pp. 3243-3256.
IEEE DOI 2307
Training, Uncertainty, Computational modeling, Knowledge engineering, Task analysis, Target recognition, cycle consistency BibRef

Li, N.[Na], Zhao, X.B.[Xin-Bo],
A Strong and Robust Skeleton-Based Gait Recognition Method with Gait Periodicity Priors,
MultMed(25), 2023, pp. 3046-3058.
IEEE DOI 2309
BibRef

Li, C.X.[Cheng-Xian], Shao, M.[Ming], Yang, Q.[Qirui], Xia, S.[Siyu],
High-precision skeleton-based human repetitive action counting,
IET-CV(17), No. 6, 2023, pp. 700-709.
DOI Link 2310
computer vision, convolutional neural nets BibRef

Li, X.J.[Xin-Jie\], Xu, H.J.[Hui-Juan],
Repetitive Action Counting with Motion Feature Learning,
WACV24(6485-6494)
IEEE DOI 2404
Representation learning, Training, Noise, Dynamics, Collaboration, Task analysis, Algorithms, Video recognition and understanding BibRef

Bacharidis, K.[Konstantinos], Argyros, A.[Antonis],
Repetition-aware Image Sequence Sampling for Recognizing Repetitive Human Actions,
ACVR23(1870-1879)
IEEE DOI 2401
BibRef

Yoo, C.H.[Cheol-Hwan], Yoo, J.H.[Jang-Hee], Kim, H.W.[Ho-Won], Han, B.[ByungOk],
Simple Yet Effective Approach to Repetitive Behavior Classification based on Siamese Network,
ICPR22(2993-2999)
IEEE DOI 2212
Training, Location awareness, Smoothing methods, Manuals, Behavioral sciences, Classification algorithms BibRef

Zhang, Y.H.[Yun-Hua], Shao, L.[Ling], Snoek, C.G.M.[Cees G. M.],
Repetitive Activity Counting by Sight and Sound,
CVPR21(14065-14074)
IEEE DOI 2111
Visualization, Analytical models, Codes, Estimation, Cameras, Pattern recognition BibRef

Kim, W.[Woojoo], Shin, E.[Eunsik], Xiong, S.P.[Shu-Ping],
User Defined Walking-In-Place Gestures for Intuitive Locomotion in Virtual Reality,
VAMR21(172-182).
Springer DOI 2108
BibRef

Celozzi, E.M., Ciabini, L., Cultrera, L., Pala, P., Berretti, S., Daoudi, M., del Bimbo, A.[Alberto],
Modelling the Statistics of Cyclic Activities by Trajectory Analysis on the Manifold of Positive-Semi-Definite Matrices,
FG20(351-355)
IEEE DOI 2102
Skeleton, Trajectory, Training, Manifolds, Measurement, Task analysis, Computational modeling, Human activity analysis, Riemannian manifolds BibRef

Zhang, H.D.[Huai-Dong], Xu, X.M.[Xue-Miao], Han, G.Q.[Guo-Qiang], He, S.F.[Sheng-Feng],
Context-Aware and Scale-Insensitive Temporal Repetition Counting,
CVPR20(667-675)
IEEE DOI 2008
Videos, Feature extraction, Benchmark testing, Estimation, Training, Context modeling BibRef

Babaee, M.[Maryam], Li, L.W.[Lin-Wei], Rigoll, G.[Gerhard],
Gait Energy Image Reconstruction from Degraded Gait Cycle Using Deep Learning,
WiCV-E18(IV:654-658).
Springer DOI 1905
BibRef

Babaee, M., Li, L., Rigoll, G.,
Gait Recognition from Incomplete Gait Cycle,
ICIP18(768-772)
IEEE DOI 1809
Image reconstruction, Legged locomotion, Gait recognition, Training, Feature extraction, Measurement, Convolutional Neural Network BibRef

Huang, S., Ying, X., Rong, J., Shang, Z., Zha, H.,
Camera Calibration from Periodic Motion of a Pedestrian,
CVPR16(3025-3033)
IEEE DOI 1612
BibRef

Boutaayamou, M., Brüls, O., Denoël, V., Schwartz, C., Demonceau, M., Garraux, G., Verly, J.G.,
Segmentation of gait cycles using foot-mounted 3D accelerometers,
IC3D15(1-7)
IEEE DOI 1603
accelerometers BibRef

Levy, O.[Ofir], Wolf, L.B.[Lior B.],
Live Repetition Counting,
ICCV15(3020-3028)
IEEE DOI 1602
Count Repetitions of an action. BibRef

Zecha, D.[Dan], Lienhart, R.[Rainer],
Key-Pose Prediction in Cyclic Human Motion,
WACV15(86-93)
IEEE DOI 1503
Cameras BibRef

Camacho-Bello, C., Báez-Rojas, J.J.,
Krawtchouk Moments for Gait Phase Detection,
CIARP14(787-793).
Springer DOI 1411
BibRef

Sedmidubsky, J.[Jan], Valcik, J.[Jakub], Balazia, M.[Michal], Zezula, P.[Pavel],
Gait Recognition Based on Normalized Walk Cycles,
ISVC12(II: 11-20).
Springer DOI 1209
BibRef

Liu, J.Y.[Jian-Yi], Zheng, N.N.[Nan-Ning],
Partitioning Gait Cycles Adaptive to Fluctuating Periods and Bad Silhouettes,
ICB07(347-355).
Springer DOI 0708
BibRef

Peternel, M., Leonardis, A.,
Visual learning and recognition of a probabilistic spatio-temporal model of cyclic human locomotion,
ICPR04(IV: 146-149).
IEEE DOI 0409
BibRef

Zhang, Z.H.[Zong-Hua], Troje, N.F.[Nikolaus F.],
3D Periodic Human Motion Reconstruction from 2D Motion Sequences,
GenModel04(186).
IEEE DOI 0406
BibRef

Thangali, A.[Ashwin], Sclaroff, S.[Stan],
Periodic Motion Detection and Estimation via Space-Time Sampling,
Motion05(II: 176-182).
IEEE DOI 0502
BibRef

Zhou, H.Y.[Hui-Yu], Wallace, A.M., Green, P.R.,
Tracking periodic motion using Bayesian estimation,
ICPR04(IV: 725-728).
IEEE DOI 0409
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human ID Using Gait, Recognition of People Through Gait .